Ninth inning, ahead or behind

Ninth inning, tied game

Other innings

Summary

The data

There have been many attempts to find evidence of clutch hitting. All of these attempts focus on the same basic principle: compare a player's performance in timely situations to his overall performance, and determine if that difference is more than expected from random. This has been done in the following ways:

• correlations of career performances in odd years to performances in even years
• year-to-year correlations
• distribution of differences compared to the binomial

In every case, the result is the same: yes, clutch hitting exists. There is no question: clutch hitting does exist. Indeed, as long as you make humans the central participants in contexts that change wildly, it will be a foregone conclusion that the results will not be completely random from our expectation of those participants. Therefore, that we find the existence of clutch hitting is not terribly exciting. It is expected. However, we haven't established the degree to which it exists, nor have we established the likelihood that we can even find the thing that we know exists.

The test of clutch hitting with the most clarity for illustrative purposes was produced by Nate Silver in Between The Numbers (p. 29), using a method popularized by Keith Woolner: for each player, compare the gap in performances in clutch and non-clutch situations, and total it based on odd years and even years. The idea is that the average gap in the odd years should be roughly the same as the average gap in the even years, for each player. This method does a nice job of removing the age and aging bias. The result is a correlation of r=0.33. The number of PA required in the sample was a minimum of 2500 for each set of even and odd years. We can estimate that the average size of each set to be PA = 3500. In order to get a correlation of r=0.33, with trials=3500, we can produce this equation:

   r=PA/(PA+7000)

This equation means that if you had 7000 PA in each sample, you would get a sample-to-sample correlation of r=.50. If you had PA=3500, then the correlation would be r=.33. For purposes of ballplayers, we usually just focus on a few years. After all, it doesn't help us to know if Bobby Abreu is a clutch hitter at age 35. We want to know this early on. Realistically, you would want to compare a two-year sample to another two-year sample. That would mean each sample would have some 1000 or 1200 PA. And using our equation above, this would mean we'd get an r=.15.

What does this mean? Well, whatever results your analysis shows as to how much clutch the sample shows, our best estimate of the true rate would be 15 percent of the sample rate. So, if you have figured out that someone has a sample of +13 clutch runs per 600 PA in the clutch (and that is a very very high figure), the regressed value would yield a +2 runs estimate as our true clutch talent. Other attempts as documented in a chapter written by Andy Dolphin in The Book, and on my site yields a similar 2 run estimate. My equation was:

   r = clutchPAs / (clutchPAs + 1250) 

   r = PA / (PA + 6250) 

For all intents and purposes, this equation is an almost perfect match to the equation derived from Woolner/Silver. Basically, if you want to find a player's clutch talent level, you cannot look at his clutch numbers. The sample size simply cannot give you the certainty we need. Clearly, we need to get our noses out of our spreadsheets and watch a game.

Watching a game

Last year, I proposed The Great Clutch Project, which reads in part:

Certainly, we can and should accept that Clutch exists in some form and to some extent—not everything that happens is random variation spinning around a constant centered mean. Even so, there is a limit to how much a clutch skill can change your mean center point. No amount of Clutch will make anyone want to choose Marco Scutaro over Alex Rodriguez. Even if Scutaro is the clutchiest player ever, and A-Rod is the biggest choker ever, when a manager has A-Rod on deck and Scutaro on the bench, he is not going to call back A-Rod to put in Scutaro. It simply won't happen. So, even if we grant that the clutch skill exists, its practicality is limited to the extent that it can exist. No one believes that the clutch skill is big enough that he would really choose Scutaro over Rodriguez. Jeter over Rodriguez, though? Maybe. So, the questions are: How big is the clutch skill; and, in practical purposes, how far can Clutch vault a player over a better hitter who doesn't have as much?

Realizing that the numbers are of no help to me in determining who is a clutch hitter, I instead turned to the fan. After all, it is the fan that most believes in clutch hitting, and it is the fan who knows a clutch hitter when he sees one. So the project started:

The first task is to find such pairs of hitters for each team. It wasn't easy. I polled the blogosphere and ended up with over 2,200 votes.

The fans on each team ended up picking a clutch hitter (best exemplified by Jeter, Dustin Pedroia and Placido Polanco), while I picked strictly by the numbers (Rodriguez, JD Drew, Curtis Granderson). I ended up with 36 Clutch players as voted by the Fans, and 36 better overall and less clutchy players, as selected by a forecasting system. Obvious picks that both sides wanted (e.g., Albert Pujols, Vladimir Guerrero, Chipper Jones) were discarded. The forecasting system estimated that, clutch aside, my hitters were .020 wOBA points better than those that the Fans selected. And so, we ended up with:

So, much like Ginger, my hitters have a sizeable advantage. You might think this is not fair, but in each and every case, the Fans preferred their choice to mine. It's their bed, people. Except that, the Fans' picks have some intangible quality, like Mary-Ann possesses. And the Fans believe that this intangible quality, this clutch factor, is enough to propel their picks to be at least equal to, if not better than, my picks when the game is on the line.

We have a situation here where both sides agree that, overall, my hitters are better. But, even given that, the Fans decided that their pick would perform better in clutch situations. (A clutch situation is where the Leverage Index is at least 2.0, which occurs roughly 10 percent of the time.)

The results

I called on David Appelman at Fangraphs to track the results for me. And he very generously did. First, let's see how both groups did overall. My hitters had an 11 point advantage in OBP and 46 point advantage in SLG. Clearly my guys produced better, overall. In wOBA speak, this is roughly a 21 point advantage for my players. Indeed, this is pretty much exactly what the forecasting system expected. That is, before the season started, the forecasting system expected my guys to hit 20 points better than the Fans' clutch players, overall. And they did.

But, how did both groups do with the game on the line? First thing I noticed is that my guys got alot of IBB. In order to be fair, I removed IBB from consideration when looking at OBP. So the results are as follows: my guys had a six point advantage in OBP and a 27 point advantage in SLG. In wOBA-speak, that translates to around a 12 point advantage for my team over the Fans' team. So, I think we can say that, yes, the Fans did have some insight into picking clutch players, but it was nowhere near enough to overcome the talent gap I started with. That is, while we can accept that "Fans know clutch", they don't know the extent of clutch. That extent is roughly 10 wOBA points (which is 10 OBP points and roughly 15 SLG points).

Is that a big deal? Well, it's less than the platoon advantage, which is 20 wOBA points. So, when you give consideration to wanting a clutch hitter at-bat, you have to temper your enthusiasm with the understanding that that clutch skill is less than if you had a similar batter with the platoon advantage. No one is going to select Marco Scutaro over Alex Rodriguez. The two players must be pretty close to begin with in talent, before you go off having a preference for your clutch hitter over someone who is otherwise a better hitter.

Fan bias

One thing that was interesting is the kinds of playes Fans considered clutch. Overall, both our teams had a bit over 19,000 PA. Both had around 970 doubles and 80 triples. But my guys had almost 300 more homeruns, and 600 fewer singles. My guys had 500 more walks and 1000 more strikeouts. As I noted in the summary to this project on my blog:

The guys they selected as clutch put the ball in play (excludes HR) 76 percent of the time, compared to my great hitters of 67 percent, in all situations. Those numbers dropped 2 percent points for both groups in clutch situations. The selection criteria by the fans on this basis was nine standard deviations from the mean, showing a fantastically clear bias in this regard. It’s very possible that to a fan, clutch is all about doing what Carlos Beltran didn’t do in his last at-bat against the Cards, when he took strike three.

The Fans have a clear bias as to what they think is clutch: put the g-dd-mn bat on the g-dd-mn ball. This bias is best exemplified by Reds fans, as I noted before the season started:

The Reds Fans detest their best hitter (Adam Dunn) so much that they actually selected four different hitters ahead of him. Every time I would check the results, a new leader would emerge. Ken Griffey Jr., Scott Hatteberg and Brandon Phillips each would have made a fine choice, but the task will be taken up by Edwin Encarnacion. (And Javy Valentin was just behind Dunn in fan appreciation.)

In the end, the Fans' bias is the main insight we gain from this project. The other insight is that the extent of perceived clutch does not match the reality of the impact of clutch. The Fans wanted their clutch hitters batting, even if they were 20 points worse than my hitters. And they lost. But, they didn't lose by 20 points, just by 10 points. Color me somewhat impressed.

Technical sticklers

For you party poopers, one standard deviation given 1900 PA is 12 wOBA points. So, the observed 10 point clutch skill that the Fans perceived won't pass any statistical significance tests. The expectation is that if I were to rerun this project for the 2009 season (which I won't), is that the Fans would not be so lucky. But, let's not let this technical detail get in the way of the partial win for the Fans.

Let's let this clutch debate end today (please?), and simply agree that: a) yes, clutch exists, b) yes, fans can perceive clutch players, but c) the impact of clutch players is limited to less than the platoon advantage.

Click here to learn about THT's download subscriptions.]]> Tom M. Tango 2009-02-20T04:30:15+00:00 http://www.hardballtimes.com/main/article/tim-raines-case-for-the-hall-of-fame/ http://www.hardballtimes.com/main/article/tim-raines-case-for-the-hall-of-fame/#When:05:19:15 Last year, I started a website dedicated to the baseball idol of my youth, Tim Raines. I invited several like-minded fans to contribute their thoughts and analysis to the site and we also scoured the Internet for articles that highlighted his achievements. I contributed three articles to make the case for putting Raines into the Hall of Fame, and I've merged them into one article here. I hope it helps you understand Raines' strong case for the Hall.

Leadoff hitters

Tim Raines is regarded first and foremost as a leadoff hitter. But is being considered one of the greatest leadoff hitters of all-time enough to warrant being in the Hall of Fame?

In order to answer that question, we need to know what is a Hall of Fame leadoff hitter. Thanks to Retrosheet, we have play-by-play statistics from 1957 to 2006 (except 1999). (Author's note: since that was written, Retrosheet has expanded by a couple of years.) What if we grabbed all the Hall of Fame leadoff batters for the Retrosheet years? (I put in a minimum of 300 career plate appearances as the No. 1 hitter to exclude part-timers, pinch hitters, and pitchers.) There are actually 17 Hall of Famers who batted leadoff, with a total of 41,181 plate appearances, led by Lou Brock's 8,644, Paul Molitor's 7,291, Luis Aparicio's 5,740, and Wade Boggs' 4,360. These four Hall of Famers account for 63 percent of the totals. Included in the 17 are such stars as George Brett (614 plate appearances) and Willie Mays (307 plate appearances). Remember, we are only looking at performances while batting in the leadoff position.

Some may not be happy with that kind of definition. Notably absent are Rickey Henderson and Pete Rose, so I created a second group of Hall of Fame type players. The criteria was: (1) at least 2000 plate appearances in the leadoff slot, (2) in the Hall of Fame or likely to be in the Hall of Fame, (3) excluding shortstops and Richie Ashburn. I included this last requirement because shortstops are voted in large part for their fielding, and a large portion of Ashburn's career took place prior to the Retrosheet years. Based on these criteria, here are the ten best players to satisfy this Hall-worthy list:

Leadoff PA
12,605	Rickey Henderson (21 percent of the total)
10,686	Pete Rose
8,644	Lou Brock
7,291	Paul Molitor
6,111	Craig Biggio
4,367	Ichiro Suzuki
4,360	Wade Boggs
2,117	Joe Morgan
2,084	Derek Jeter
2,068	Barry Bonds (3 percent of the total)

After these players, we have some solid players, like Brett Butler, Kenny Lofton, Willie Wilson, and Johnny Damon. Therefore, I think it's fair to say that these 10 players are the best leadoff hitters of the Retrosheet years. Note that even if you quibble with some of the players lower down for whatever reason, they comprise a small total (3 percent for Bonds). The bulk of the leadoff plate appearances from this group were accumulated by Rickey Henderson (21 percent).

How does Raines compare to the Hall group and the Hall-worthy group?

 Batting Average
 .296 Hall-worthy
 .295 Raines
 .289 Hall

 Slugging Percentage
 .428 Hall-worthy
 .428 Raines
 .400 Hall

On Base Percentage
 .386 Raines
 .378 Hall-worthy
 .355 Hall

RBIs (prorated)
531 Hall-worthy
523 Raines
469 Hall

Runs (prorated)
1010 Raines
 997 Hall-worthy
 894 Hall

Raines scored 13 more runs than the Hall-worthy group, and drove in eight fewer runs. That's about as dead-even as you'll find. Tim Raines performed above the level of Hall of Famers, and at a similar level to Hall-worthy players. Take a big part of Rickey Henderson and Pete Rose, add a good size part of Lou Brock, Paul Molitor, and Craig Biggio, and stir in some Ichiro Suzuki, Wade Boggs, Joe Morgan, Derek Jeter, and Barry Bonds, and you get a composite that is a shade inferior to Tim Raines.

No. 3 hitters

As we've seen, Tim Raines compares favorably to Hall of Fame leadoff hitters. But, Raines also spent a substantial portion of his time as a No. 3 hitter. Surely, he can't hold his own with those hitters, right? Wrong.

There have been 26 Hall of Fame players who hit in the No. 3 slot in the Retrosheet Years with at least 1500 career plate appearances. Of those, there are eight that we can classify as non-power hitters:

PA of No. 3 non-power hitters
5,760 Roberto Clemente
5,517 Kirby Puckett
5,196 Tony Gwynn
3,897 Paul Molitor
3,564 Joe Morgan
3,045 Rod Carew
2,451 Robin Yount
2,314 Wade Boggs

A hitter tries to accomplish three things: get on base, move runners over, and not make an out. He needs to get on base to score runs, and he needs to move runners over to drive home runs. All the while, he's trying to minimize his outs to keep the inning alive.

Tim Raines, as a No. 3 hitter, made 978 batting outs. Here is how our two Hall of Fame groups and Tim Raines did as a No. 3 hitter, pro-rated to 978 batting outs:

Runs RBIs (pro-rated)
 226 222 Hall of Fame Power Hitters
 245 189 Tim Raines
 218 207 Hall of Fame non-Power Hitters

For those who prefer Runs Produced (i.e., Runs Participated In, R+RBI-HR), here you go:

RP (pro-rated)
405 Tim Raines
392 Hall of Fame non-Power Hitters
379 Hall of Fame Power Hitters

How did Raines manage to hold his own with such great hitters? Here are their batting averages, SLG, OBP, and modified OPS (modified OPS is 1.8*OBP + SLG, a measure that more closely aligns itself to overall run production than OPS):

  BA     SLG     OBP     mOPS
.292	.513	.379	1.195 Hall of Fame Power Hitters
.318	.449	.409	1.184 Tim Raines
.320	.470	.384	1.162 Hall of Fame non-Power Hitters

And even when compared to the Hall of Fame Power Hitters, Raines can hold his own. He has a 30 point advantage in OBP, compared to a 64 point disadvantage in SLG. That small difference (remember, an OBP point is worth 1.8 times more than a point in SLG) evaporates when we consider Raines' superiority in basestealing. It's this overall combination of getting on base, moving runners (including himself) over, and not making an out that allowed Raines' to participate in more runs than your standard No. 3 Hall of Fame hitter.

Contemporary Hall of Famers

So far, we've compared Tim Raines as a leadoff hitter and as a No. 3 hitter to other Hall of Famers in those batting slots. We focused only on their respective hitting stats in those batting slots. And he stood shoulder-to-shoulder with them.

Now, let's compare the entirety of Tim Raines' career to those of contemporary Hall of Famers. I drew a line at anyone born since Brock was born. This gives us a list of 22 Hall of Famers. Here they are ranked by Runs Produced (i.e., Runs Participated In, R+RBI-HR):

Runs Produced, All Contemporary Hall of Famers
3208	Carl Yastrzemski
3040	Eddie Murray
3037	Dave Winfield
2911	Cal Ripken
2861	George Brett
2855	Paul Molitor
2787	Robin Yount
2690	Reggie Jackson
2553	Mike Schmidt
2545	Tony Perez
2515	Joe Morgan
2409	Wade Boggs
2386	Tony Gwynn
2361	Lou Brock
2347	Rod Carew
2260	Willie Stargell
2230	Carlton Fisk
2097	Ryne Sandberg
2078	Johnny Bench
2022	Ozzie Smith
1949	Kirby Puckett
1926	Gary Carter

Tim Raines had 2381 Runs Produced, which places him in the middle of the pack of greats, between Tony Gwynn and Lou Brock.

These Hall of Famers averaged 10,862 plate appearances, which is extremely close to Raines' 10,359 plate appearances. Raines earned just 503 fewer plate appearances than the group average, and puts him between Rod Carew and Tony Gwynn's career. If not for his battle with Lupus, he would have certainly come to bat more often.

Tim Raines also reached base 3977 times, via hit, walk, or hit batter. (He actually exceeded the 4000 level, if you include reaching base on error.) The Hall of Fame group average was 3908, which is 69 less than Raines, despite those players having 503 more plate appearances than Raines. The players ahead and behind Raines in times reached base are Reggie Jackson and Tony Gwynn, respectively.

You notice a repeating trend here? Tony Gwynn and Tim Raines, while somewhat comparable on a skill-by-skill basis, end up being extremely equal when looking at their impact to generating runs. And Tony Gwynn was a shoo-in for the Hall of Fame.

These 22 Hall of Famers had a .288 batting average, compared to Raines' .294. While they had a 30 point advantage in slugging percentage (.455 to .425), Raines had a 24 point advantage in OBP (.385 to .361). Again, Raines is right in the middle of the Hall of Famers. He is simply getting there in different ways.

The one place where Tim Raines stands head and shoulders above the group is in basestealing. Tim Raines not only stole 808 bases, but he was caught stealing only 146 times. The net bases gained on steals is 662. The best basestealers from this Hall of Fame group are Lou Brock (938 SB, 307 CS, 631 net bases) and Joe Morgan (689, 162, 527). If Tim Raines stole 130 more bases and was caught stealing 161 more times, he'd equal Lou Brock's performance. That's how bad a basestealer Raines would have to be to bring himself down from his high perch, down to Lou Brock's very high level.

One objection to being compared to these 22 players is that they are not necessarily the best comparison group. After all, can we really compare Raines to Gary Carter and Ozzie Smith? We can try to whittle the list down a bit. Let's remove all catchers, shortstops, and first basemen, players who earn a substantial bonus for their fielding, or have to overcompensate with their hitting to make up for plugging up the first base position. We do away with Carter, Bench, Fisk, Ozzie, Ripken, Stargell, Murray, and Perez, leaving us with 14 Hall of Famers. If we pro-rate the performance of the remaining 14 (shown as HOF14 below) to Raines' 10,359 plate appearances, this is what we get:

Raines  HOF14    
2381    2409    Runs Produced
        
3977    3819    Times On Base
2605    2699    Hits
 713     840    Extra Base Hits
1330    1079    Walks
 662     188    SB - CS

.294    .296    BA
.385    .370    OBP
.425    .457    SLG
1.119   1.124	modified OPS (1.8*OBP + SLG)

Raines is just 28 runs produced behind these players, despite playing a substantial portion of his career as a leadoff hitter (runs produced slightly favors middle-of-the order hitters). While these more offensive-minded hitters had a 32 point advantage in slugging, Raines had a 15 point advantage in OBP. "Modified OPS" is a measure that more closely aligns itself to overall run production than OPS, and we see that the 32 points on one side almost perfectly match the 15 points on the other. And this disregards Raines' basestealing completely.

Conclusion

The difference between comparing to groups, as opposed to one-on-one comparisons, is that we are no longer fascinated by milestones like 3000 hits, or .300 batting average. Immortality is not about achieving some arbitrary rounded-number milestone. This is especially true in this case, since baseball is not about getting hits, but about generating runs. It's runs that leads to wins, not hits. Hits is just one component of creating runs. Extra base hits, walks, and steals are the other main components.

While individually, Raines is unlike his peers, overall, it's hard to distinguish them. Any time we compare Raines to a reasonable group of Hall of Famers, we always end up with the same thing: Raines is just like them. If you have a group of players worthy of the Hall, and an individual player compares very favorably to that group, you have a Hall-worthy player by definition. That is what Tim Raines is: the definition of a Hall of Famer. Whether Raines is compared to the best of the best leadoff hitters or the best No. 3 hitters or the best players of his era, he stands among them. And they stand in the Hall of Fame.

Click here to learn about THT's download subscriptions.]]> Tom M. Tango 2008-12-04T05:19:15+00:00 http://www.hardballtimes.com/main/article/jack-morris-babblefest-2009/ http://www.hardballtimes.com/main/article/jack-morris-babblefest-2009/#When:05:52:15 Another year, another day to talk about the Hall of Fame candidate Jack Morris, known as the "pitcher of the decade".

The best pitcher of the 1980s. I’m sure you’ve heard that plenty, right? About Jack Morris? Just take some arbitrary 10-year period, preferably bordered by round numbers, like the “80s”, and you have your test of greatness. Why not “pitcher of the decade”, and take something like 1976-1985? Or 1986-1995?

1980-1989

Anyway, let’s get back to 1980-1989. In that time period, Jack Morris led the league with 2443.2 innings. Let’s set the minimum innings pitched qualifier to 80 percent of that (1955 innings). We actually get back only 11 pitchers. Among those pitchers, Morris is seventh in ERA+! Unquestionably, the pitcher of the 1980s was not Jack Morris, but David Stieb, with the era-leading ERA+ of 127, far ahead of No. 2 Bert Blyleven. Here’s the full list.

Jack Morris’s W/L record was 162-119, a win percentage of .577. But, that’s No. 2 behind Bob Welch’s .596. Stieb was third at .562. Steib was also 2nd in innings.

What’s better? The No. 1 (by far) in ERA+, No. 2 in innings, and No. 3 in win percentage, or the No. 1 in innings, No. 2 in win percentage, and No. 7 in ERA+? I know this sounds like a trick question, but it’s not! There’s been mountains of ink spilled by people claiming, proudly, that it’s the latter.

1981-1990

Let’s shift things by one year: 1981-1990. The innings leader (Morris again) was 2443.1, making the threshhold level 1955 innings. Now, Stieb looks even better, bumping his ERA+ by two points to 129, compared to Morris’ 108. His win percentage is .593 while Morris is at .569. Heck, Morris doesn’t even compare to Bob Welch (ERA+ of 115). Welch was 150-90, compared to Morris’ 161-122. You get 11 more wins with Morris, but 32 more losses too! Morris had a 3.70 ERA compared to Welch’s 3.17.

If you want to give Morris credit for most wins for the decade, fine, go ahead. But, don’t call him the best pitcher for the decade. Stieb is definitely ahead of him. And with Welch, Nolan Ryan, Bert Blyleven, Charlie Hough and Fernando Valenzuela around, Morris is lucky to even be considered better than any of those guys for “best” pitcher of the 80s.

Cy Young Voting

How did Jack Morris do in the Cy Young Voting in the 1980s? He received votes in five different seasons, to tie Dan Quisenberry for the lead. They are followed by Fernando Valenzuela, Dwight Gooden, Orel Hershiser, Nolan Ryan,and Mario Soto, each receiving Cy Young votes in four seasons.

If you go by total Cy Young points (five for first place, three for second place, one for third place), the leader is Steve Carlton (280), followed closely by Roger Clemens, and Bret Saberhagen. Quisenberry is fourth. Jack Morris is in 22nd place.

The former method gives you one total point if you received votes from anyone. The latter method gives you alot more points the more first place votes you get. As a result, each is biased to one extreme or the other. In either case, Jack Morris takes a back seat to Dan Quisenberry.

Pitcher of the 1950s

When you select an arbitrary time period, like the 1980s, it is biased toward whatever player happened to get his peak years in the 1980-1989 time period. You could have someone with better years from 1981-1990 (as Stieb unquestionably tops Morris under this guideline), and you can have someone else be better in the 1977-1986 time period. The point of selecting the time period is to compare Jack Morris to his peers. To that end, what I like to do is compare players based on birth year. Morris was born in the middle of 1955, and so, his contemporaries would be pitchers born between 1950 and 1960.

Among those pitchers, Morris is fourth in innings, just behind Dennis Martinez and Frank Tanana. He's way ahead of the next guys (Doyle Alexander, Orel Hershiser, Bob Welch), and even more behind the leader (Bert Blyleven). In terms of quantity, our best comparisons for Morris are Martinez (born 1955) and Tanana (born 1953). Here are Morris and Martinez:

               Morris   Martinez
Wins-Losses   254-186    245-193
ERA              3.90       3.70
IP              3,824      4,000

According to Baseball Reference, Martinez compiled his 3.70 ERA in a league and parks where the average pitcher would have had a 3.93 ERA, while Morris did his 3.90 in an environment of 4.08 for an average pitcher. So, Martinez was 0.23 ERA better than average and Morris was 0.18 ERA better than average.

Summary

Dennis Martinez may not be the pitcher of the 1980s, but he's had a career performance that is very close to Jack Morris. Dave Stieb may be the pitcher of the decade, while Jack Morris is just one of many names in the running, along with Bob Welch. Dan Quisenberry was considered a better pitcher by the contemporary writers over Jack Morris.

These are the number of Hall of Fame votes each of these pitchers received in their first year of eligibility:

111 Morris      (9 years on the ballot, and counting)
 58 Hershiser   (2 years on the ballot, then removed)
 18 Quisenberry (1 year on the ballot, then removed)
 16 Martinez    (1 year on the ballot, then removed)
  7 Saberhagen  (1 year on the ballot, then removed)
  7 Stieb       (1 year on the ballot, then removed)
  1 Welch       (1 year on the ballot, then removed)

Other than the "1980s" tag, what exactly vaults Morris over every one of his peers? Is it the memory of the 1991 World Series, while ignoring the 1992 World Series? Unfortunately, we'll be talking about Jack Morris every two months until 2014. Stieb, Martinez, Hershiser at al will be nothing more than afterthoughts until their obituaries are written.

A different Hall of Fame awarding system would keep all these very fine pitchers, including Morris, in the forefront, and all would be discussed simultaneously. As blogger Patriot has pointed out, the Hall of Fame discussions are now focused almost entirely on who was left out, rather than who is now in. Who are we going to be talking about in one month: Jim Rice and Jack Morris, or Dave Winfield and Nolan Ryan? Who has had more ink spilled in their names, the guys who are actually considered better, or the guys who just keeping hanging on?

Click here to learn about THT's download subscriptions.]]> Tom M. Tango 2008-11-13T05:52:15+00:00 http://www.hardballtimes.com/main/article/momentum-shmomentum/ http://www.hardballtimes.com/main/article/momentum-shmomentum/#When:07:22:15

Background

Joe Sheehan was allowing the possibility that chemistry exists (it does, so no reason to pretend it doesn’t) and that he may have found it. Did he? Chemistry and momentum are two of those things that people will point to after something good has happened, and will forget about if something bad has happened. This is like you go to Vegas, tell your friends you won $10,000, conveniently forgetting about the previous time you went when you lost $20,000. There are other examples in sports. Like the Pujols/Lidge/Oswalt contrarian situation. Or the more famous Don Cherry too-many-men situation, or Ronaldo’s injury making everyone on the team bleak before the final game, or the stolen World Series in 1985. We tell the $10,000 wins stories a lot more than the $20,000 losses.

Now, let’s say that you somehow are god-like, and know momentum when you see it. How much is it worth? As we know, a superstar like Albert Pujols is worth some seven wins, per 162 games, above replacement. That means that if you have a .500 chance of winning with an average team and bad first baseman, adding Pujols will make it a .550 team. Something like that. If you have a great pitcher, C.C. Sabathia or Doc Halladay, you turn a .500 team into a .625 team. How much can momentum be worth? Can it possibly cancel out the Rays bringing in C.C. Sabathia or Roy Halladay? Can it even cancel out bringing in Grady Sizemore or Pujols or Joe Mauer? Is momentum even worth Willie Bloomquist?

One of my readers suggested that in-game momentum should be easier to find, and have more impact, than day-to-day momentum. Makes sense. After all, as Earl Weaver once said, momentum is tomorrow's starting pitcher.

The Study

I looked for every single game in the last 50 that a home team came back from at least five runs down to tie the game and end the inning. This means that, starting the top half of the following inning, we've got two teams tied, each with an equal number of outs to go, except the home team now has the momentum. What happened? How often did the team with the momentum win the game?

By my count, there are 230 such games, of which the team with the momentum won 126 times, for a win percentage of .548. That's pretty good. But, is this simply the home field advantage speaking, or is it momentum? Here is how the home team did, when coming back from down by five runs, down by between one and four runs, and tied (i.e., they didn't score in the inning of a tied game):

Down by...  Eventually wins...
5 or more  .548
1 to 4     .528
0          .524

So, we see that there is a mild gain to momentum: a +.020 improvement in winning. Given 230 games, one standard deviation is .033. One cannot even claim that the result has much statistical significance (less than one standard deviation from the mean). But, I know most of you don't want to talk about statistical significance. Let's take the results for what they are: the home team, if it is down by at least five runs but ends the inning tied, will eventually win the game 54.8% of the time, while in all other non-momentum situations, they will win about 52% or 53% of the time.

Let's repeat, but this time with the road team having the momentum. Note that entering the bottom half of the inning means that the home team has three extra outs for the game. So, we should expect to see lower win numbers for the road team. Down by five or more to tie the game entering the bottom half of the inning, the road team eventually wins 67 of their 153 games, or a win percentage of .438. Here is the full chart for the road team having momentum:

Down by...  Eventually wins...
5 or more  .438
1 to 4     .396
0          .403

We have a similar situation here as with the home team: The effect of momentum is an extra .035 wins or so.

Summary

If we combine the two results (home and road), we get that the team with the momentum wins 193 out of 383 games, or .504. We need to compare that to some baseline. Since there were 230 games with the home team having the momentum, and we expect them to win around .524 times without momentum, then the number of non-momentum wins per 230 games would be roughly 120.5 wins. Similarly, with 153 road games with the momentum, we would have expected around .403 wins with no momentum, or 61.5 wins per 230 games with no momentum. Combining the two baselines, we have 182 wins in 383 games, or a .475 win percentage rate expected with no momentum.

If I also consider the inning, the baseline level is a .467 win percentage expected with no momentum. The difference between the actual win percentage with momentum (.504) and no momentum (.467) is +.037 wins. (This figure is almost 1.5 standard deviations from the mean.) That is the extent of the momentum effect, in-game. Momentum is, at best, like getting one superstar player on your team. (Statistical significance tells us it is much less than that. But, I know you don't want to hear that.)

And don't forget that we're talking extreme momentum here; in-game momentum in which the team scored five runs in an inning to tie the game. One must believe that the effect of momentum must be even less day-to-day.

Momentum exists. But we'll be hard-pressed to find it in anything other than in-game scenarios. We can barely find it with the numbers in even the most dramatic come-from-behind games. All you have left to do is enjoy the moment, without having to explain it. If you really have the need to tell people that you have found momentum, then here’s what you do: Find 10 games from now for the next 12 months that you think has momentum or chemistry written all over it. Bet on the game. Then, come back here, on November 1, 2009, and tell me how much money you made. And I don’t want to hear only from the winners.

Click here to learn about THT's download subscriptions.]]> Tom M. Tango 2008-10-23T07:22:15+00:00 http://www.hardballtimes.com/main/article/the-fans-scouting-report/ http://www.hardballtimes.com/main/article/the-fans-scouting-report/#When:05:20:15 Background

I introduce the Fans' Scouting Report project on my site the same way every year:

Baseball's fans are very perceptive. Take a large group of them, and they can pick out the final standings with the best of them. They can forecast the performance of players as well as those guys with rather sophisticated forecasting engines. Bill James, in one of his later Abstracts, had the fans vote in for the ranking of the best to worst players by position. And they did a darn good job.

There is an enormous amount of untapped knowledge here. There are 70 million fans at MLB parks every year, and a whole lot more watching the games on television. When I was a teenager, I had no problem picking out Tim Wallach as a great fielding 3B, a few years before MLB coaches did so. And, judging by the quantity of non-stop standing ovations Wallach received, I wasn't the only one in Montreal whose eyes did not deceive him. Rondell White, Marquis Grissom, Larry Walker, Andre Dawson, Hubie Brooks, Ellis Valentine. We don't need stats to tell us which of these does not belong.

What I would like to do now is tap that pool of talent. I want you to tell me what your eyes see. I want you to tell me how good or bad a fielder is. Go down, and start selecting the team(s) that you watch all the time. For any player that you've seen play in at least 10 games in 2008, I want you to judge his performance in seven specific fielding categories.

Comparisons

I asked several people in MLB front offices for their reactions to the results from the Fans' opinions. None was comfortable having his/her views broadcast, other than to say that the results were somewhat consistent with their internal evaluations. That is, while they are not necessarily great, they are certainly not crap either. And certainly, they are better than what you hear from the mainstream.

While I would like to be persistent for my own indulgence in getting a hold of their scouting reports and publishing them side-by-side with the Fans' results, I realize that my wishes simply can't materialize. At least, not until the player retires. What I would like to do is to compare the results of the Fans, the Wisdom of the Crowd, to the perceptions of the closest thing we can come to a professional scout.

For this installment, I leaned on Dave Cameron of USS Mariner to provide his scouting report, which I would then compare side-by-side to the Fans. Why Dave? He has a loyal following of readers, and that is for a good reason: quality attracts quality. He does great work on his blog and follows the minor leagues intently. He's opinionated and doesn't follow the company line. Dave was a fairly natural choice for me.

Here are the simple averages of Dave and the other 100+ Mariner ballots I received, for each player, sorted by the Fans' results from best to worst:

Fans	Dave	diff	Player
4.57	3.86	0.71	Suzuki, Ichiro
4.44	4.14	0.30	Beltre, Adrian
3.50	3.00	0.50	Reed, Jeremy
3.18	2.86	0.32	Bloomquist, Willie
2.93	2.86	0.07	Burke, Jamie
2.92	3.71	-0.79	Cairo, Miguel
2.90	2.86	0.04	Balentien, Wladimir
2.80	2.29	0.51	Lopez, Jose
2.79	2.57	0.22	Johjima, Kenji
2.78	2.57	0.21	Betancourt, Yuniesky
2.63	3.14	-0.51	LaHair, Bryan
2.33	2.14	0.19	Wilkerson, Brad
2.31	1.86	0.45	Ibanez, Raul
2.24	1.86	0.38	Clement, Jeff
1.92	2.00	-0.08	Sexson, Richie

2.95	2.78	0.17	AVERAGE

Overall, we see that the Fans are a bit more optimistic than Dave. This is a basic trend we see on all ballots, across all teams. On a 1-5 scale, where 3 is average, the average ballot for the league comes in at 3.25. This is not really a big deal, if we can accept that each team has around the same amount of favoritism (remember, it is mostly the teams' fans that cast ballots); all I do is (basically) reduce the rankings eight percent from each ballot, and I'm lined up. Dave here offers the sober view of 2.78, and the Fans being 0.17 higher than that is consistent with the favoritism we expected.

Generally speaking, to convert the 1-5 rating into a run value score, you follow this quick formula:

Runs = (Rating minus 3.25) * 15

Beltre for example is +18 runs by the Fans and +17 by Dave (for Dave, we subtract 3.0, not 3.25, since we don't suspect bias in his results, as noted earlier). So, recasting the above chart into a Runs chart, we get:

Fans	Dave	diff	Player
19.9	12.9	+7.0	Suzuki, Ichiro
17.8	17.1	+0.7	Beltre, Adrian
+3.7	+0.0	+3.7	Reed, Jeremy
-1.1	-2.1	+1.0	Bloomquist, Willie
-4.9	-2.1	-2.8	Burke, Jamie
-4.9	10.7	-15.6	Cairo, Miguel
-5.2	-2.1	-3.1	Balentien, Wladimir
-6.8	-10.7	+3.9	Lopez, Jose
-6.9	-6.4	-0.5	Johjima, Kenji
-7.1	-6.4	-0.7	Betancourt, Yuniesky
-9.3	+2.1	-11.4	LaHair, Bryan
-13.9	-12.9	-1.0	Wilkerson, Brad
-14.2	-17.1	+2.9	Ibanez, Raul
-15.1	-17.1	+2.0	Clement, Jeff
-19.9	-15.0	-4.9	Sexson, Richie

I should also note that for this article only, I'm treating each of the seven categories the same. Obviously, some traits are more important than others, especially when you consider the position.

Difference of opinion

So, where are the big differences? Miguel Cairo is easily the biggest difference maker, with a 16-run difference in evaluation. In six of the seven categories, Dave is either a bit more, or a lot more, optimistic. And in the seventh one, he is a shade less optimistic. Specifically, with Release/Footwork, Dave gave Cairo a "5", meaning "great", while the Fans came in at 3.0, meaning "average". It is a substantial difference of opinion. Even so, if this was the only difference, it would only make up a 4-run difference.

In Instincts, Dave had him as a 4, and the Fans at 3.0; First Step, Dave 4 again, and the Fans 2.8; Speed: 3 to 2.7; Hands: 4 to 3.3; Arm Strength: 3 to 2.6. Only with Arm Accuracy is there a disagreement, with Dave at 3 and the Mariner fans at 3.1. Once you factor in the extra fan bias, Dave is more optimistic as well. So, Dave definitely sees something, across the board, that the Fans either don't see or don't appreciate, to that extent. Bryan Lahair poses a similar across-the-board issue, for an overall difference of 11 runs. It should be noted that Cairo and Lahair are bench players, with 38 and 19 "games" each, as of Aug 20, 2008. ("Games" is innings divided by nine.) So, Fans may simply not be that confident in their evaluations of such players.

After those players, the only major source of disagreement is Ichiro. For each of the five previous years that I have run the Fans' Scouting Report, since its inception in 2003, Ichiro has led all players. Each and every season. And in 2008, while Ichiro is one of the three leading vote-getters, he is no longer the shoe-in that he was in the past years. Fans are notorious for being a few years behind the curve, because of the halo effect. Ken Griffey Jr. was above average by the fans until 2007, at which point he finally was considered below average. And this year, he dropped even more. Robbie Alomar had a similar issue a few years ago. Time and again, really good fielders in their 20s are simply being evaluated now in their 30s by Fans as still being of high quality. Ichiro is no exception.

Dave however, sees a chink in the armor. In six of the seven traits (excluding arm accuracy), the Fans gave Ichiro a flat 4.6 or 4.7 across the board. It is an appreciation (blind love?) like no other player enjoys. In those six categories, Dave has given Ichiro a 4 in five of them, and a 5 in one of them (Speed), for an average of those six traits of 4.2. If that was the extent of it, it would not be a big deal.

But, it is the seventh category where the difference lies, Arm Accuracy. Fans give him a 4.2, while Dave gives him a 2. That is an enormous difference of opinion. Of the 88 fans who cast a ballot on Ichiro's arm, only three rank him as a 1 or 2. In addition 14 have given him a 3 (average). So, only 17 of the 88 fans (almost 20 percent) can be seen to generally agree with Dave. Indeed, 35 of the 88 fans, or 40 percent, gave him the highest possible mark of a 5.

If I were to repeat this exercise for every team, I am sure I'd find similar situations: There's a couple of players with wide disagreement, one or two with some disagreement, and the rest all within a five-run or less agreement. This is fairly powerful. For example, is Xavier Nady really a fantastic hitter as his 2008 data shows? Or merely a pretty good hitter that his last few years has shown? We can say that his 2008 performance "disagrees" with his career performance. That is, the reliability of his 500 PA in 2008 is not a strong enough indicator to represent his true hitting talent. The same applies with the Fans' Scouting Report. We will always have a few exceptions, be it with the observational data from the eyes, or the performance data from the bat. Our expectations should be tempered by the reality of the limits in our capabilities.

Conclusion

There is no conclusion. This project is a never-ending project that attempts to collect, aggregate and present observational data in some meaningful form, every single year.

For example, ten years from now, when someone asks who was Derek Jeter similar to, you can tell him Michael Young. The current generation knows little of Gary Pettis' fielding accomplishments. Those of us from the last generation know the wonder of his fielding talents. While he has his Gold Gloves as a testament to his fielding talents, what about everyone else? And, the generation before me talks about Mickey Stanley and Paul Blair, but the recollection of their exploits will continue to diminish over time.

This is why this project is important. It can stand the test of time, and be another view into a player's accomplishments. It can act as an historical record of the contemporaneous view of the Fans. I urge all to spread the word on their blogs. I would say that four out of five bloggers I contact directly agree to spread the word, so I consider myself lucky that I have found a strong base of support. Please participate on my site by filling out a ballot.

Click here to learn about THT's download subscriptions.]]> Tom M. Tango 2008-09-11T05:20:15+00:00 http://www.hardballtimes.com/main/article/changing-on-the-fly/ http://www.hardballtimes.com/main/article/changing-on-the-fly/#When:04:10:15 If there's one thing I share with Wayne Gretzky, it's that our favorite sport is baseball:

Trevor, a catcher, has his father excited because of his promise as a baseball player. "I live through him quite a bit because my dream was to play baseball," Gretzky said.

As luck would have it, we were both born Canadian, and so, our first exposure to life was a hockey stick. And the rest was history. Well, he's a part of history. All I can do is enjoy it. And I do love baseball and hockey.

There may be some sports that can match hockey for its sheer power, but none can match it for its combination of speed and power. And, in a sport that prizes and values power and hitting as much as hockey does, even they have limits. Remember that, as I continue.

There is one thing that all sports share: action and tension. If you take out the action from sports, and if you take out the tension, is there any reason at all to watch? As long as you have one, you have a captive audience. If you have both, your audience is enthralled. But, neither? Well, now the sport is simply being arrogant in thinking that they're doing a good job by giving the fans nothing but dead time.

What follows are some rule changes that were discussed on my blog. They are designed to reduce the dead time, or preserving the health of players. I'm not necessarily the originator of each of these rules, but I have been in one or two cases. I've tweaked some, and am basically championing all of them. If you like the rules, then go to my blog to see who had the idea. If you don't, then just blame me.

Rule Change No. 1: The Mid-Inning Relief Change Penalty

Except for perhaps the first mid-inning relief change, is there a bigger drag to the sport? Look what happens in other sports. In the NHL, teams used to switch goalies as a way to get a timeout. The players would take practice shots on goalies to buy even more time. The NHL also has penalties for dumping the puck into the stands. In the NBA, it's foul after foul, and getting guys to the line.. The NFL loves to kill tension by having as many commercials as possible, even inventing the commercial late-game break.

Baseball's bullpen is so deep, and relievers are so specialized (the LOOGY, left-handed-one-out-guy), that teams take it to the extreme. And all the while, there is no action or tension. The best way to reduce or eliminate this is to do what the NHL does: penalties. Hockey doesn't tolerate crap like tripping, hooking and other activities that limit the action. You want action, you want tension.

So, what you do is allow one mid-inning switch per game. But, the second time you do a mid-inning switch (same inning or not) is start the batter at 1-0. And the third time, the batter starts at 2-0. You may find it hard to believe, but a batter who starts at 2-0 hits as well as Albert Pujols does at 0-0.

Is there any manager in his right mind that would dare make three mid-inning pitching changes in the same game? I don't even see a reason one would do it more than once. Any subsequent mid-inning change puts the batter at 3-0. This is a severe penalty, granted. But, this eliminates the dead time, and continues the tension. This is a rule with no downside.

Rule Change No. 2: The 4-0 Walk Penalty

Vladimir Guerrero is up at bat. He is prepared, with his batting gloves wrapped tightly around the bat, to swing at anything close to the plate. Anything! And still, teams will intentionally walk him. Was there a more tension-reducing sight than when Barry Bonds was coming to the plate with 1B open? This is the complete opposite of what should have happened, and was not what the fans paid to see.

The rule is simple: Any 4-0 walk, intentional or not, results in a two-base penalty. If you have a runner on 2B, the 4-0 walk gets you runners on 1B and 3B. If you have a runner on 3B, then it's guys on 2B and 3B. And, with runners on 2B and 3B, the batter goes to 1B, the runner on 2B stays put, and the runner on 3B scores.

Under this scenario, how often would a pitcher not give the batter at least one strike? Again, fans win, and the players go back to giving us action and tension.

Rule Change No. 3A: The One-and-Done DH

The DH is nice as a fallback option, to let Vladimir rest, and so on. But, no one really likes the one-dimensional aspect of it. Edgar Martinez, David Ortiz, Travis Hafner and Frank Thomas, potentially being in the Hall of Fame, as they accumulate a substantial portion of their careers as a DH? The NFL-style rule, of having eight fielders and nine hitters isn't appealing, as you have no diversification.

You want some variety. Every sport has that. The NHL has the pure scorers, the power forward, the checking forward, the rushing defensemen, the stay-at-home defensemen. They all have their value. The same for the NFL and NBA. But, nine bashers, and eight glove wizards? No thanks.

The current DH rule gives you a glimmer of dread of that happening. But, what if the DH can come in for only one at-bat, without the pitcher being removed from the game (as it would be with a PH)? That's the one-and-done DH. The DH can come in for any player, with the manager deciding whether the starting player stays in the game (meaning our one-and-done DH is out of the game), or the starting player is removed (meaning that we have our traditional PH becoming a defensive substitute). And you can have as many one-and-done DH as you want in each game.

Does this help the action and tension? Not directly. But, unless Micah Owings is pitching, you'll need at least four one-and-done DH per game. With eight starting fielders, and a backup catcher, that's already 13 non-pitchers out of your 25-man roster. You'll need at least 14, maybe 15 non-pitchers on your team. With the earlier rule change limiting the number of mid-inning pitching changes, we can get back to 10 pitchers per team. In order to preserve your bench, you may even let your best hitting pitcher (who may not even be pitching that day) come in to bat as a one-and-done DH, in a two-out, bases-empty situation.

Rule Change No. 3B: The Floating DH

As an option to tweaking the existing DH rule, we can continue with our David Ortizes and Travis Hafners, but they can bat for anyone in the lineup, and can come up no quicker than one time per every nine team at-bats. So, if No. 8 hitter Adam Everett is up with men on base, you can bring in your floating DH to bat for him.

But, that means that the next batter, the pitcher, must bat for himself. The floating DH must wait for at least eight more batters to come up, before being brought in. This may drastically increase the value of a guy like Vladimir Guerrero, by taking him off the field of play, and provide the flexibility of floating him to bat in someone's place when you need him the most. This would also imply that the first time he comes to bat would be no earlier than the 9th batter of the game. It's a small tweak with drastic implications.

Rule Change No. 4: The Hit Batter Misconduct

Once again inspired by the NHL, who don't take kindly to the cowardly practice of its competitors: Any pitch that hits a batter in the head is an automatic ejection, regardless of intent. Basically, it's a reckless play to throw the pitch at, or near, the batter's head. And that's not the kind of tension we need.

Any hit batter will move all runners one base. If there are no runners on base, then the hit batter puts the batter at second base. There's really no reason to allow a pitcher to hit the batter. I know all about the tough pitchers in the 1960s throwing inside and being indignant at the idea of taking away part of the plate. But, a tougher sport like hockey takes exception to such reckless and flagrant use of a weapon. A pitcher is not being tough by having a weapon his opponent doesn't.

Rule Change No. 5: The Pickoff Called Ball

This was more of an issue back in the days when Tim Raines was getting pickoff throws left and right. Fans love basestealing. And for the most part, basestealing is inconsequential. And pickoff throws do nothing to advance the game's tension, and it rarely has any action to it. So, how will it work?

The first pickoff throw, per batter, is a freebie. Any other pickoff throw is treated like a pitchout: a called ball, if the runner is not picked off. You can also put a commit line, whereby if a runner takes a lead that crosses that line, the pickoff penalty rule no longer applies. This way, if a guy takes an overly aggressive lead, then the pitcher won't be penalized for trying to pick him off. It's a dare by the runner to pick him off, and in this case, we want to see the pitcher trying to pick him off. This would also have the by-product of eliminating the balk rule. It's a horrible rule that is no longer needed.

Rule Change No. 6: The Home Plate Commit Line

The NHL doesn't allow their goalies to be touched. The NFL hardly allows their quarterback to be touched. Why is a catcher at the mercy of the runner, especially since his focus is away from the runner for the key moment in time? The catcher is not a doll to be pummeled, regardless of how much fun it is to see a guy with minimal equipment (compared to hockey and football) be knocked down.

You put a commit line thirty feet from home plate. Once a runner crosses that line, it becomes a force play at home plate. Now, the catcher simply needs to touch the base, and not worry about tagging the runner.

Ridiculous? Tough-minded hockey GMs (many of whom are former players) are seriously considering changing the icing rule. The way it currently works in the NHL is that when a player dumps the puck down ice, the opposing defenseman and a forward on the dumping team race to the puck as it crosses the (extended) goal line. If the defenseman gets to the puck first, it's icing. If not, the play is still on.

It's a pure speed and physical confrontation moment, that sometimes leads to injuries. Injuries are part of the game, but in this case, the defenseman has his back to the forward. And in hockey, behind the back plays is highly frowned upon. So, a suggestion by these tough guys is to treat the goal line as a commit line. If the defenseman crosses the line first (without needing to touch the puck), it's icing.

This keeps the speed aspect of it. Confrontations can still happen, when the forward passes the defenseman, and now the defenseman must take aim. In this case, however, the forward is in a better position to take the hit. Another option is to put the commit line 30 feet from the boards (the goal line is about 10 feet). This gives both players enough time to stop, once they cross the line.

Conclusion

If hockey-loving folks, who will do anything to keep the physical parts in the game, talk about eliminating unnecessary parts of the game, the other leagues should pay attention. It’s apparent that at some point, you have to sacrifice the confrontations, for the health of the player, even at the highest level of professional sports. At one point, goalies didn't even wear masks, as the culture at the time treated them as cowards if they dare don protection.

Drastic rule changes of any kind in any sport to protect any player is always looked at, and encouraged. Not baseball. Any such attempts are shouted down as ridicule. Welcome to the NHL, circa 1950. Really, if I was proposing rule changes in the NFL or NBA, there would be little to no resistance. "Baseball" makes things different. Eventually, MLB will wise up, just as every other sport has.

Keep the action and tension in the game, while preserving the health of the players. Who can disagree?

This trade-off is one way to look at that intangible skill known as Clutch. Certainly, we can and should accept that Clutch exists in some form and to some extent—not everything that happens is random variation spinning around a constant centered mean. Even so, there is a limit to how much a clutch skill can change your mean center point. No amount of Clutch will make anyone want to choose Marco Scutaro over Alex Rodriguez. Even if Scutaro is the clutchiest player ever, and ARod is the biggest choker ever, when a manager has ARod on deck and Scutaro on the bench, he is not going to call back ARod to put in Scutaro. It simply won't happen.

So, even if we grant that the clutch skill exists, its practicality is limited to the extent that it can exist. No one believes that the clutch skill is big enough that he would really choose Scutaro over ARod. Jeter over ARod, though? Maybe.

So, the questions are: How big is the clutch skill; and, in practical purposes, how far can Clutch vault a player over a better hitter who doesn't have as much?

Choices

In baseball, this choice is best represented by Kevin Youkilis, a very good hitter with the Red Sox, and Mike Lowell, another good hitter with the Red Sox who also possesses an intangible to step up his game when the situation warrants it (or so the story goes). To be sure, we are talking about two choices that are in the same ballpark—Mrs. Howell might have even better intangibles, but no amount of Clutch on her part will sway the fans.

We're also not talking about the Jessica Alba of the Red Sox, the player who possesses both the obvious sizeable appeal and the hard-to-define intangibles. When the game is on the line, David Ortiz is everyone's choice.

An interesting contrast to Mary-Ann would be Lindsay Lohan, an even more attractive choice than Ginger. In some circles, the intangibles of Ms. Lohan might be enough to undo her appeal, enough to put her at the same level as Mary-Ann. However, as far as Red Sox fans are concerned, Manny being Manny is still a slightly better pick than Mike Lowell. So, although Manny's talent level is much higher than Lowell's, there's a limited gap that Lowell's (positive) intangibles can cross to try to overcome Manny's (negative) intangibles.

And so, we are left with a fair fight: Ginger or Mary-Ann. Lowell or Youkilis.

The Nash Equilibrium

The first task is to find such pairs of hitters for each team. It wasn't easy. I polled the blogosphere and ended up with over 2,200 votes. Half the teams had a Jessica Alba choice: Both sides wanted Ortiz, Vlad, Chipper, David Wright, Pujols, Hanley Ramirez, Matt Holiday, and on and on. These guys, as their team's best hitters and with no close competition with superior intangibles, were discarded from the competition. You might remember the scene in A Beautiful Mind, where John Nash postulated:

If we all go for the blonde and block each other, not a single one of us is going to get her. So then we go for her friends, but they will all give us the cold shoulder because no one likes to be second choice. But what if none of us goes for the blonde? We won't get in each other's way and we won't insult the other girls. It's the only way to win. It's the only way we all get laid.

If the clutch-believers can freely choose their players and the just-gimme-the-best believers can freely choose their players, we will end up selecting the same players half the time, thereby making the exercise moot. We need to get rid of those guys that the two sides believe is both the best hitter in a clutch situation and the best hitter overall. The Nash equilibrium forces us to go to the next group of hitters on those teams. In some cases, such as the Braves with Teixeira and McCann (i.e., Scarlett Johansson and Beyonce), I had to go to the fourth best Fan pick (Jeff Francoeur) to go against the team's fourth best hitter (Kelly Johnson). And Astros fans basically want their best hitters at the bat the whole time.

DISCARDED PICKS

ANA	Vladimir Guerrero
ARI	Conor Jackson
ATL	Chipper Jones
ATL	Mark Teixeira
ATL	Brian McCann
BOS	David Ortiz
BOS	Manny Ramirez
COL	Matt Holliday
COL	Todd Helton
COL	Brad Hawpe
CWS	Jim Thome
CWS	Paul Konerko
FLA	Hanley Ramirez
HOU	Lance Berkman
HOU	Hunter Pence
HOU	Carlos Lee
HOU	Miguel Tejada
KC.	Billy Butler
MIN	Joe Mauer
MIN	Justin Morneau
NYM	David Wright
NYM	Moises Alou
NYM	Carlos Beltran
PIT	Jason Bay
SD.	Adrian Gonzalez
STL	Albert Pujols
TB.	Carlos Pena

On other teams, however, the choice was very clear. Derek Jeter is so beloved by his fans, and ARod, one of the best hitters of his generation, is so... not, that Jeter easily was the Fans' choice. In this case, ARod's Lohan lost out to Jeter's Mary-Ann. So, we add this pair to our competition.

The Reds Fans detest their best hitter (Adam Dunn) so much that they actually selected four different hitters ahead of him. Every time I would check the results, a new leader would emerge. Junior, Scott Hatteberg and Brandon Phillips each would have made a fine choice, but the task will be taken up by Edwin Encarnacion. (And Javy Valentin was just behind Dunn in fan appreciation.) Step right up, Edwin and Adam.

Sometimes, the fight is very close, like with the Phillies. Ryan Howard is a better hitter than Chase Utley, but only ever so slightly. And the Fans, quite forcefully, preferred Utley as their game-on-the-line pick. So, the Fans want Utley, and I want Howard, so that's what we get. The tightest race was with the Brewers, where Braun is a slightly better hitter than Fielder, but Fielder was slightly more desired by the Fans.

The Molina brothers are beloved by the Fans as well. With Pujols out of the competition, Yadier Molina had it easy with the Cardinals. But the Giants Fans also voted overwhelmingly for their own Molina (Bengie), even though none of their players were discarded. Randy Winn, Ray Durham and Aaron Rowand (a gamer if ever there was one) barely registered a blip with the Giants fans. Clearly, fans need to be emotionally tied to a player before they can grant him Clutch.

And on and on it went, team by team. Ibanez is the Mariners' best hitter (barely), but he was nowhere to be found underneath the avalanche of Ichiromania. Magglio Ordonez, a great hitter, is the Fans' pick against an even better hitter, the newly minted Miguel Cabrera.

I went through all 30 teams, and the Fans' picks are listed below against my picks.

In every case, the guy on the left is the Fans' pick; the guy on the right is the team's best hitter that was not discarded. We have a Nash equilibrium, since both sides are happy with their choices, even if it means that both sides must leave the (same) best option on the table.

In addition, I put in some wild cards (see table below). This is most easily explained with Rollins vs. Burrell. Not only did Phillies Fans not select Burrell (a better hitter than Rollins)—many also voted Burrell as "POOR" (the last guy they want with the game on the line). And they loved Rollins so much, he was almost preferred to Ryan Howard. Clearly, a Rollins-Burrell match-up satisfies our needs here.

Another hated player was J.D. Drew; the Red Sox Fans simply don't want to see him. And whom did the they prefer? Dustin Pedroia, a slightly worse hitter. Take him, with my compliments. Mariners Fans were aghast at the thought of Richie Sexson, so we'll take him on our team. They can have Yuniesky Betancourt, whom they seem to really, really like.

In each of the cases below, the Fans had a huge gap in love between the pairs of players.

So, much like Ginger, my hitters have a sizeable advantage. You might think this is not fair, but in each and every case, the Fans preferred their choice to mine. It's their bed, people. Except that, the Fans' picks have some intangible quality, like Mary-Ann possesses. And the Fans believe that this intangible quality, this clutch factor, is enough to propel their picks to be at least equal to, if not better than, my picks when the game is on the line.

Technical Notes

wOBA is an overall measure, akin to OBP, except that it weights each event properly. OBP assigns a value of 1.0 to each of a walk, single, and home run; wOBA recasts OBP by giving a walk 0.72, a single 0.90, a double 1.24, a triple 1.56, and a home run 1.95. If I just scared you, just think of wOBA as OBP and you'll be fine. As you can see, the overall wOBA of the Fans' picks is some 20 points behind that of my picks.

Also, I should point out that "my" picks are really the picks of Marcel The Monkey Forecasting System. It's a fairly accurate system that has taken on all comers and stood shoulder-to-shoulder with them. The listed wOBA is Marcel's expectation for 2008 for these 72 hitters.

Fangraphs will be tracking these performances on a daily basis. In technical terms, a game is "on the line" when the Leverage Index (LI) of the plate appearance is at least double the average. So, stop in every few days, and let's see how they do.

Yeah, but...

I know, I know, you have two questions. I won't bother writing your questions, since they are so obvious.

To answer your first question: Comparing a player's performance in high-leverage situations to his performance in other situations was already covered in The Book. Andy Dolphin wrote a whole chapter on it. Long story short, clutch skill exists, but it is very hard to detect in the performance numbers. My study is not related to the question of comparing a player to himself.

To answer your other question: What I'm tackling here is a question of fan perception, of how much Clutch do the Fans believe exists. Clearly, although the Cardinals Fans thinks of Molina as a super-duper clutcheroo player, that is not enough of an edge to vault him over Pujols. Even a clutch believer has a certain level of sanity. That's what I'm after here. And, on the basis of the above tables, the perception seems to be that the clutch skill can make up for about 20 points of wOBA. That is not insignificant, but it certainly is not a whole lot—just enough to turn Mike Lowell into Kevin Youkilis, or Edwin Encarnacion into Adam Dunn. That's their perception.

Now, we'll see if even that perception has any basis in reality. Does Mary-Ann have any intangibles, or should we have stuck with Ginger all along?

Click here to learn about THT's download subscriptions.]]> Tom M. Tango 2008-04-03T04:11:15+00:00 http://www.hardballtimes.com/main/article/changes-in-home-run-rates-during-the-retrosheet-years/ http://www.hardballtimes.com/main/article/changes-in-home-run-rates-during-the-retrosheet-years/#When:04:04:15 I make no bones about it. Retrosheet is a monumental achievement, and equally effective in diminishing worker productivity in corporate America.

The Colorado Rockies did two things in 1993: They were part of the expansion of the league, and they brought with them a park that decidedly favored hitters. The expectation is that home runs would explode. From 1988 through to 1992 (pre-Rockies, pre-expansion), 593,975 times a batter had a plate appearance in which he put bat to ball (i.e., all PA, excluding strikeouts, walks, hit batters, interference and sacrifice bunts), and 16,001 went for home runs. That's 2.7 percent of all contact PA going for homers.

From 1993 through to 1997, we had 584,918 contact PA (a virtual match to the previous time period), with 21,019 home runs, for a rate of 3.6 percent. That's 33 percent more home runs compared to 1988-1992. Furthermore, the year-by-year rates in each group were fairly stable. It was a sudden jump from one time period to another, occurring right at 1993, perhaps 1994. It was sudden, it was quick, it was dramatic. And by definition, it was a one-time shift.

What can cause changes in output? There are five groups of events:

1. Player-controlled events (drugs, conditioning)
2. Umpire-controlled events (strike zone)
3. Team-controlled events (parks, ballplayers)
4. League-controlled events (expansion, configuration of ball, bat)
5. Nature-controlled events (climate, wind, weather)

Hitters, pitchers, and parks

Here are the HR rates from 1982 through to 1998:

1982	2.7%
1983	2.7%
1984	2.7%
1985	3.0%
1986	3.2%

1987	3.7%

1988	2.7%
1989	2.6%
1990	2.8%
1991	2.9%
1992	2.5%

1993	3.1%

1994	3.7%
1994	3.7%
1995	3.6%
1996	3.9%
1997	3.7%
1998	3.7%

You will note that 1987 stands out, both in terms of the huge jump from 1986, and an even more dramatic fall to 1988. The difference between 1987 and the 1993-1998 period is that what happened in 1987 was a sudden jump, which reverted back, while 1993 was a sudden jump and sustained itself from 1994 onward.

So, when trying to explain 1987, we can discard the first group of events (player controlled). It's implausible that hitters, as a group, would be able to do anything in the offseason of 1986 to give them a decided advantage over pitchers. Anything they would have done to their minds, bodies and souls would have resulted in a more gradual shift over a period of years. We can also discard the third group, since again, even if teams had decided that the Vince Colemans and Willie Wilsons and Gary Pettises of the world were not for them, this wouldn't happen suddenly in one offseason.

That leaves us with the likely culprits of: mother nature, juicing the ball, and the umpires calling a different strike zone. Each of these possibilities satisfies the conditions that something could happen overnight, and it could be undone a year later.

When it comes to 1993, these three remain as plausible scenarios (with mother nature likely being a less reasonable answer; comparisons to minor leagues at the time would be instructive). However, now we also have to consider the parks and expansion. Can we figure out how much effect those two had? Remember my ode to Retrosheet?

Enter Retrosheet

What if we reconstruct our league totals, but we throw out all the expansion hitters and expansion pitchers? While we're at it, let's throw out all the expansion parks. What we'll be left with in 1993 is the same group of players from 1992, and the same parks. But, that's still not enough, since not each hitter will face the same pitcher in both years, or for the same number of PA, or even in the same parks.

Let's go back to 1987 for a second. In that season, Tim Raines hit two home runs against Doug Drabek at Olympic Stadium on six contact PA. In 1988, he hit zero home runs on two contact PA. For Raines, 1987 was a good year, but it was a bad one for Drabek, in terms of this tiny sample. Pro-rating Raines' 1987 down to two PA, he had 0.67 HR in 1987 and 0 in 1988, on two PA. What we did here was look for the exact same hitter and the exact same pitcher in the exact same park with the exact same number of (pro-rated) PA, over back-to-back seasons. We are in effect controlling for the players and the parks, leaving only one parameter (the year) as variable.

If we repeat this step for all hitters, pitchers, and parks in 1987/1988, we end up with 29,212 matching PA, which is roughly 25 percent of the entire population of plate appearances in those seasons. While the other 90,000 or so discarded PA are some combination of non-matching hitters/pitchers/parks between 1987 and 1988, these 29,212 PA are an exact match of hitters/pitchers/parks in those two years. The result here will tell us exactly the effect between those two seasons.

In 1987, the matching combinations had 4.1 percent of their contact PA as home runs, while it was 2.9 percent in 1988. If we look at the non-matching data for those two seasons, the home run rate was 3.6 percent and 2.6 percent, respectively. We can see, therefore, that the two groups mirrored themselves: The matching combinations dropped their homer rate by 30 percent, while the non-matching dropped it by 28 percent. We really didn't expect a different answer. It's not like we had a new Coors field to contend with, or a bunch of new hitters and pitchers to throw things off.

The Coors expansion

Ah, but what about 1992/1993? In this case, we had 22,622 matching PA, with 629 homers in 1992 and 744 in 1993. Remember, we are looking only at the same hitters against the same pitchers in the same parks with the same number of PA here. And we see a jump of 18 percent in number of home runs hit. The rest of the league had a jump of 24 percent. So, we can see two things here: 1. There was a huge jump in homers, without considering parks or expansion, at 18 percent 2. The rest of the league jumped even more, at 24 percent, with the extra jump possibly because of parks and/or expansion.

Continuing with 1993/1994, there was a 20 percent jump in homers for the matching combination, with only a 15 percent jump for the non-matching combination. If we combine the two groups of years, 1992/1993 and 1993/1994, we get a 42 percent jump with the matching combination of players, and a 43 percent jump with the non-matching combination of players.

It would seem, therefore, that the league-wide jump in home runs between 1992 and 1994 (43 percent) affected the matching combination and the non-matching to a nearly identical extent. That is, while the matching combination only considered the same players in the same parks in back-to-back years, and the non-matching considered everything else (new players, new parks, etc), the overall effect was the same.

The conclusion would then follow that expansion and parks had very little to do with the jump in homers between 1992 and 1994.

The real culprit

I don't know. But, juicing the ball seems to be a possibility. Bob DuPuy of MLB, using his best legal training, says this:

“We are satisfied that the ball comports with all major league specifications,” DuPuy said. “Beginning in 2000, we have had annual independent testing done by UMass at Lowell, baseball research center, under the direction of Dr. James Sherwood, and those tests have showed full compliance with standards.”

You know he's trying to tell you something without telling you anything by using technical words: specifications, independent, compliance, standards and doctor. Talk about a lawyer pitching his perfect game: five key words in only two sentences! Very impressive. And, he tells you nothing at all, which must have gotten him the lawyer-of-the-day at his local club. Now, consider MLB's own ball tester, whom Mr. DuPuy was kind enough to include in his statement, Dr. James Sherwood:

That’s part of what frustrates Sherwood… “Their testing window is this big,” he says, his hands a foot apart. “I don’t know why it was ever set that wide.” A ball testing at the high end could travel as much as 50 feet farther than one falling on the low end, he says. That’s the difference between a lot of home runs and a whole lot of home runs. Sherwood would love to bring the testing procedures into the modern era. Upstairs, his computerized machines can control a baseball bat with the precision of Barry Bonds. He has air cannons that can fire a ball at 180 mph. But the league doesn’t like change. Sherwood estimates the MLB hasn’t altered ball design since Babe Ruth played. Sherwood says there’s some evidence that firing a baseball at 58 mph may not be fast enough to accurately determine its liveliness. “Has there actually been data on that?” Drane asks. “Yeah,” Sherwood says, “We’re just going to explore looking at the higher speeds and present that to the league. Maybe they’ll change their minds.”

Mr. DuPuy must have known about Dr. Sherwood's thoughts, no? DuPuy is the contact person for MLB, and Sherwood is the contact person at the testing facility. They must have contacted each other at some point, no? Does Mr. DuPuy's statement represent Dr. Sherwood's position? In a pure legalese sense, I suppose it does. But, by the reasonable man test? Not at all.

Now, consider the most dedicated baseball researcher alive regarding the home run, Greg Rybarcyzk of HitTracker Online, when he says:

In 2006 there were 1,454 homers in the “Just Enough” category, which means clearing the fence by approximately 10 feet or less. They are spread fairly smoothly from 0-10 feet of clearance.

Greg allows us to estimate that at 8.7 feet of clearance, 1,265 homers would have cleared the fence. That is, if you can change the composition of the ball to reduce the length of the long flyball by 8.7 feet, you'd end up with 1,265 fewer home runs. In 2006, we had 5,386 home runs on 135,626 PA, for a rate of 4.0 percent. Knocking out 1,265 would bring the homer rate down to 3.0 percent. The average home run rate from 1982-1992 was 2.9 percent. So, all we have to do is reduce the length of homers by around 8.7 feet, and we'll get home run output rates from the 1980s. Is this possible? Quoting the good doctor once more:

The Major League balls are manufactured in Costa Rica and have a compressed cork sphere per the specifications. The Minor League balls are manufactured in China and have a cork center as specified in “1996 Minor League Baseball Proposal”. This cork center is the likely source for the decrease in performance, which results in a comparable Minor League ball hit of 391.8 ft under the same conditions as the Major League balls. Small samples of 1998 MLB baseballs were also tested. The 1998 MLB baseball had a comparable batted-ball distance of 400.5 ft.

And 400.5 feet minus 391.8 feet equals 8.7 feet.

Click here to learn about THT's download subscriptions.]]> Tom M. Tango 2008-02-15T04:04:15+00:00 http://www.hardballtimes.com/main/article/fielding-aging-curves/ http://www.hardballtimes.com/main/article/fielding-aging-curves/#When:04:15:15 Retrosheet. A sweeter single word has never been uttered.

I've been having a lot of fun going through the fielding data that has been captured by Retrosheet. It's not the subjective data, like trajectory or zone, but simply basic data, like which pitcher threw the ball to which hitter that was caught by which fielder in which park. This research resulted in a fielding system that I introduced in The 2008 Hardball Times Annual called With Or Without You (WOWY).

Analyzing baseball performance data is all about understanding the context—one look at Dante Bichette's seasonal batting lines, and any baseball fan knows this to be true. What affects whether a shortstop will convert a batted ball into an out? The three most important parameters are the identity of the pitcher, the identity of the batter, and the park. There are other considerations, such as the game state (inning/score/bases/outs), the identity of the runner(s), the guys playing to the shortstop's left and right, and the guy that he's throwing the ball to. However, the first three have the greatest impact.

We can generalize the impact of the batter. When a right-handed batter is at the plate, and the ball is in play (anywhere in the park), the shortstop will make an out 15% of the time. With a lefty batter, the out conversion rate is 9%. Clearly, the handedness of the batter matters a lot. Presuming that the shortstop will face a somewhat random collection of batters over the course of a season, let's alter one of the parameters to be the handedness of the batter (in lieu of the identity of the batter).

As it happens, we can also generalize the impact of the fielder. Today, I look at the data from the angle of the shortstop's age.

Matched Pairs

In 1976, Robin Yount was the shortstop for pitcher Jerry Augustine on 233 occasions at County Stadium in Milwaukee when a righty batter put the ball in play (excluding home runs and bunts). Thirty of those times, the 21-year-old Robin Yount made the out. In the following season, that exact same combination (Yount/Augustine/County Stadium/righty batter) was involved with 289 balls in play, of which Yount made the out 47 times. Pro-rated down to 233 at-bats (as in 1976), Yount would have made 38 outs. The only substantial difference between the two seasons is that Yount was 21 years old when he made 30 outs, and 22 years old when he made (a prorated) 38 outs.

Let's call these matched pairs: We have a duplicate set of parameters in each group, except for the age of the shortstop. All we need to do now is go through the Retrosheet years (1956-2006, excluding 1999) and repeat the work. There are 1,785 such matched pairs with the shortstop at ages 21 and 22. The total number of balls in play is 20,994, which is fairly substantial. Adding up the number of outs made by the shortstop, we have 2,721 outs at age 21 and (with these exact same shortstops, pitchers and parks) 2,750 outs at age 22. We can therefore conclude that the shortstops improved from age 21 to 22 to the tune of 29 extra outs on 20,994 balls in play. In a typical season with 4,000 balls in play, that translates to almost six more outs.

If we repeat this process with a new set of players and parks for the age 22/23 matched pairs, we get 60,439 balls in play. In this case, there is almost no change: 7,889 outs at age 22 versus 7,895 outs at age 23. In effect, the performance of these shortstops with these pitchers at these parks was virtually the same at ages 22 and 23. We therefore conclude that there was no improvement in talent. At age 23/24 we also get exactly no improvement: 82,569 balls in play, with exactly 10,754 outs at each of ages 23 and 24.

Based on this study, the defensive peak for a shortstop is between the ages of 22 and 24. Afterwards, for the matched pair at every age level, the shortstops showed a decline. The average decline is nine plays per year, which roughly corresponds to seven runs per year.

Here's the chart for the above data along with all the other age pairs:

AGE1	AGE2	PA	OUT_YR1	RATE6_YR1	OUT_YR2	RATE6_YR2	DELTA	CHAIN
21	22	20,994	2,721	0.130	2,750	0.131	6	-6
22	23	60,439	7,889	0.131	7,895	0.131	0	0
23	24	82,569	10,754	0.130	10,754	0.130	0	0
24	25	125,146	16,536	0.132	16,316	0.130	-7	0
25	26	148,236	19,497	0.132	19,438	0.131	-2	-7
26	27	157,718	20,691	0.131	20,466	0.130	-6	-9
27	28	137,983	18,268	0.132	18,002	0.130	-8	-14
28	29	127,548	16,883	0.132	16,298	0.128	-18	-22
29	30	105,921	13,998	0.132	13,702	0.129	-11	-40
30	31	87,081	11,450	0.131	11,099	0.127	-16	-52
31	32	72,144	9,495	0.132	9,276	0.129	-12	-68
32	33	64,004	8,376	0.131	8,287	0.129	-6	-80
33	34	36,877	4,967	0.135	4,950	0.134	-2	-85
34	35	24,071	3,238	0.135	3,168	0.132	-12	-87
35								-99

DELTA refers to the change in outs per 4,000 balls in play from one year to the next. CHAIN is the running total of DELTA. Start CHAIN at -6 outs per 4,000 balls in play at age 21. Then, add the DELTA in the first row (+6) to the CHAIN (-6) and put that total (0) as the CHAIN at age 22. Keep going for every row.

What this chart shows is that there's a defensive peak for shortstops from ages 22 to 24, and then a long progression downward as the shortstop ages.

Selective Sampling

Now, this is an enormous plummet. Our own expectation was a peak in the mid-20s, followed by a gentle drop of three plays or so every year; instead, we're getting a drop of nine plays per year.

The steepness is due to selective sampling: The only shortstops who survive the study are those good enough (or thought to have been good enough) to play in back-to-back years. And a player who performs above the population average is likely a player who benefited from good fortune. Consider that the league-average out rate is around 12.5%, but, for every matched pair under observation, the out rate for shortstops in the first of the two years is at least 13.0%. In essence, that 13.0% contains a tinge of luck. And that luck needs to be extracted.

Just as an illustration, let's say that we need to remove 0.15% from the sample out rate in the first year to establish the group's true talent level. (The 13.0% is a sample performance rate, a mix of true talent plus random variation.) Here's how that new chart looks:

AGE1	AGE2	PA	OUT_YR1	RATE6_YR1 (regressed)	OUT_YR2	RATE6_YR2	DELTA	CHAIN
21	22	20,994	2,721	0.128	2,750	0.131	12	-28
22	23	60,439	7,889	0.129	7,895	0.131	6	-16
23	24	82,569	10,754	0.129	10,754	0.130	6	-10
24	25	125,146	16,536	0.131	16,316	0.130	-1	-4
25	26	148,236	19,497	0.130	19,438	0.131	4	-5
26	27	157,718	20,691	0.130	20,466	0.130	0	0
27	28	137,983	18,268	0.131	18,002	0.130	-2	0
28	29	127,548	16,883	0.131	16,298	0.128	-12	-2
29	30	105,921	13,998	0.131	13,702	0.129	-5	-14
30	31	87,081	11,450	0.130	11,099	0.127	-10	-19
31	32	72,144	9,495	0.130	9,276	0.129	-6	-29
32	33	64,004	8,376	0.129	8,287	0.129	0	-35
33	34	36,877	4,967	0.133	4,950	0.134	4	-35
34	35	24,071	3,238	0.133	3,168	0.132	-6	-31
35								-36

Note that the rate column for year 1 is marked as "regressed" to avoid confusion. Now, the CHAIN column looks much more reasonable: The peak is from ages 24 to 28, and then the drop is only five plays per year. In the three years leading up to age 24, the gain averages eight plays per year.

The degree of regression will establish the peak age and the slope toward the peak age. I tried different regession values, and it always maxes out at age 28. So, that is one conclusion we can make: On average, shortstop fielding prowess peaks no later than age 28. Recall that, in the first (unregressed) table, the peak age was around 23. So, the true answer lies somewhere between these points (without regression and with maximum regression). The second chart above seems to satisfy this condition.

As for the slope toward the peak, that's another issue. I can adjust the regression so that we have very little slope (only one fewer out per year), whereas with no regression, the slope was nine outs per year. What is the real answer? I don't know yet. But, my guess is that the second chart above will likely be very close to the final best answer.

Next up: aging curves for all the other positions.

Click here to learn about THT's download subscriptions.]]> Tom M. Tango 2008-02-07T04:15:15+00:00