The hardest part of preparing strategy for a roto league is the lack of information. Even if you knew before the season the exact final statistics of your players, you would still need to put those statistics in the context of your league. 300 home runs will be enough for maximum points in some leagues, and only minimum points in others. Since I’m not attached to a site that hosts leagues, I do not have a database of league statistics to accurately forecast points for various statistical plateaus.

Fortunately, I can approximate those values with data that has been published. In their draft kit prior to 2012, ESPN published the minimum, maximum, and average benchmarks for all of their standard, 10-team roto leagues from 2009-2011. They may not help for deeper leagues or those with exotic categories, but these tables provide a solid foundation for the most common categories and league sizes.

Looking at those averages, you can see that a team that projects to have 300 home runs, 1,100 runs, 1,050 RBI, and 175 steals should expect to net 10, eight, seven, and seven roto points in those respective categories on average. You can also see that it would require about 24 runs, 42 RBI, and 26 steals to barely beat out an average nine-point team in each of those categories.

Since that example team has already reached 300 home runs, enough to earn the maximum 10 roto points, the owner can go ahead and trade away players he expects to hit home runs for players that produce in those needed categories. Since he needs just 24 runs compared to 42 RBI and 26 steals, he might be tempted to try to trade for players that score a lot of runs. However, he already expects eight roto points in runs compared to only seven points in both RBI and steals.

Really, even if the team needed the same number of runs, RBI, and steals for the same additional roto points in each category, the owner should not be indifferent to the category he trades; statistics in those categories do not occur with the same frequency. Fewer steals happen in a season than there are runs and RBI, and far fewer wins are earned by pitchers than any other traditional counting statistic.

Since I do not know which players were owned by what percentage of fantasy teams in a given season, I opted to calculate the roto point benchmarks as a percentage of the total of each statistic in the entire league. For now, I am assuming that a similar percentage of each available statistic is captured by the owned players in a fantasy league, which seems fair since each category has the same available roto points.

Here are the totals of each hitter counting statistic from 2010-2012, as well as the three-year average of each:

Season | HR | Runs | RBI | SB |
---|---|---|---|---|

2012 | 4934 | 21017 | 19999 | 3229 |

2011 | 4552 | 20808 | 19804 | 3279 |

2010 | 4613 | 21308 | 20288 | 2957 |

Avg |
4700 |
21044 |
20030 |
3155 |

And based on the ESPN averages, here are the roto point benchmarks for each category as a percentage of the league totals of each statistic:

% of Annual League Total, 2010-2012 | ||||
---|---|---|---|---|

Roto Points | HR | Runs | RBI | SB |

10 | 6.59% | 5.52% | 5.64% | 7.02% |

9 | 6.22% | 5.34% | 5.45% | 6.35% |

8 | 5.97% | 5.22% | 5.31% | 5.93% |

7 | 5.77% | 5.12% | 5.20% | 5.58% |

6 | 5.58% | 5.02% | 5.09% | 5.27% |

5 | 5.39% | 4.92% | 4.98% | 4.95% |

4 | 5.19% | 4.81% | 4.86% | 4.64% |

3 | 4.96% | 4.68% | 4.71% | 4.29% |

2 | 4.68% | 4.51% | 4.53% | 3.85% |

1 | 4.23% | 4.21% | 4.21% | 3.21% |

This table presents the relative cost of each additional roto point with every category on the same scale. You can eyeball the columns to get a sense of which categories are more dispersed than others. Standard deviation summarizes those differences in a single number for each category:

HR | Runs | RBI | SB | |
---|---|---|---|---|

StdDev | 0.72% | 0.40% | 0.44% | 1.16% |

The hitter counting stats fall into three distinct tiers. On average, runs and RBI are the easiest categories in which to gain and lose ground, stolen bases are the hardest, and home runs are in-between. That may not be enough information to determine the optimal target category for the example team, but it is enough to demonstrate that the differences in volume of needed runs, RBI, and steals does not eliminate any category from consideration in a potential trade.