A reminder about spring stats

We all know what everyone says about spring stats: They don’t count. Despite that, spring stats still can convey information about a player and they can even reflect a change in a player’s skill set. Someone who pays attention to such information can learn something prior to the season despite sample size issues, a weaker slate of opponents, and stats that are not counted for any number of reasons. It simply requires a “user beware” warning.

Leading sabermetricians don’t ignore spring stats. Why would they? It’s free information and we analytic types love information. According to BaseballPress.com’s Nate Springfield, John Dewan of Baseball Information Solutions has successfully predicted breakout campaigns at a 60 percent rate using spring training slugging percentage. You can learn more about his technique here.

With that said, the purpose of this brief article is to remind everyone that small sample sizes are particularly prone to luck. And we can see that luck via BABIP. Below are three surprising performances of the spring that have been talked about as indicative of a breakout campaign.

Asdrubal Cabrera has put together a .364/.426/.600 triple slash with three home runs. He also has a .426 BABIP.

Mark Trumbo has helped alleviate concerns about the injured Kendrys Morales by posting a .297/.316/.662 slash along with six home runs. His BABIP? .407. Two walks against 20 strikeouts is also a little worrisome.

Alex Gordon has people back on his side after throwing together a .343/.459/.729 spring with six home runs. His BABIP was .436.

This is not to say any of these players can’t or won’t break out this year. Gordon in particular still has some spiffy stats even after you normalize his line thanks to 14 extra base hits out of 24 and 12 walks against 15 strikeouts. But before we get too excited about a breakout spring potentially carrying into the regular season, we need to remember to do our due diligence. Be sure to adjust those spring numbers.

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Comments

  1. GTWMA said...

    Here’s a summary, if you can’t get to the FBC link

    I’m researching this, because if you look at their archive, they’ve created a list like this each spring since 2005. I’ll do the rest later, but here’s what I found from their 2007 list.

    They names 36 players on that list.
    22 of the 36 improved their OPS+ in 2007 compared to 2006 (for players with less than a full season, I compared their 2007 performance to their career performance; for other players, I compared 2007 to 2006). 14 of the 36 had their OPS+ decline in 2007.

    15 of the 36 had their OPS+ improve by more than 10 points; however, 7 of those 15 are questionable, because they include guys who had limited playing time. Milton Bradley’s OPS+ went from 114 to 153, but he also played in only 61 games. Similarly, big OPS+ increases by Mike Rivera, Cristian Guzman, Timo Perez, Greg Dobbs, Willie Bloomquist and, possibly, Garret Anderson, had little fantasy relevance. 9 of the 36 had it decline by more than 10 points; 3 weren’t really relevant.

    So, overall:
    8 improved by more than 10 points and had significant PT
    7 improved by more than 10 points, but had limited PT
    7 improved by less than 10 points
    5 declined by less than 10 points
    3 declined by more than 10 points, but for PT or other reasons weren’t really relevant
    6 declined by more than 10 points

    Some amount of the predictive power also seems to come from the fact that they compare players to their career slugging; thus, players that “broke out” in 2006, but still have somewhat low career slugging, fall into the list. To some extent, the spring numbers might “confirm” the 2006 breakout, but a better analysis for that would compare a group of 2006 breakout players and see whether those who showed up on this list were more likely to maintain or improve upon their prior year breakout.

  2. GTWMA said...

    A look at this method from their 2006 data is even less positive. In that year, they again identified 36 players. They break down this way:

    13 improved their OPS+ performance over prior year/career by 10 points
    4 improved their OPS+ performance over prior year/career by less than 10 points
    5 had their OPS+ performance decline from prior year/career by less than 10 points
    14 had their OPS+ performance decline from prior year/career by more than 10 points

    So, over the 2 years we now have:

    28 improved by 10+ points
    11 improved by 1-9 points
    10 declined by 1-9 points
    23 declined by 10+ points

    I’m not seeing much predictive performance here.

  3. GTWMA said...

    Finally, their 2005 set of 30 players:

    12 had an improvement of 10+ points
    3 had an improvement of 1-9 points
    5 had a decline of 1-9 points
    10 had a decline of 10+ points

    Three year totals:

    40 had a 10+ improvement
    14 had a 1-9 improvement
    15 had a 1-9 decline
    33 had a 10+ decline

    Not seeing much there.

    Yes, Matthias, you would probably want to test the statistical significance of these changes, but as we see with things like clutch hitting, none of these differences in such a small subset of at bats is in any way likely to have statistical significance. You could run a standard difference in mean score test, but it would show little. In addition, there’s nothing in their list that controls for things like park factors and such.

    All in all, I would not put much credence in this theory.

  4. GTWMA said...

    I’m wondering where the hell this 60 percent rate comes from, because I look at that list of 18 guys from 2010, and I see three that had a significant improvement over their career rates—Bautista, Rasmus, and Cruz.  Other guys had good years like Tulowitzki), but perfectly in line with what they had already done.

    Frankly, I think this system is a load, and I’d like to see the proof that it posts a 60% rate of predicting breakout from previously established levels of performance.

  5. Brad Johnson said...

    I’ll take a look into it if I get a chance during work tomorrow. It struck me as an odd result too given all the quirks that go into a spring training line (small sample/BABIP, lower level of competition, etc.). You’d think they would be looking at ISO, no?

  6. Mitch said...

    When evaluating Alex Gordon, one should carefully consider historical data that strongly suggests Alex Gordon is Alex Gordon. Alex Gordon is not a good ML hitter.

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