Power Of Consistency Part II

Originally posted June 21, 2008
Part Two of Three

My previous article detailed an experiment investigating the conventional wisdom that favors consistent hitters over streaky sluggers for Head-to-Head leagues. Click here to read that article if you missed it.

Last time the experiment showed little evidence to confirm the sensible-sounding idea that you want to skip over streaky hitters for your head to head league team. The conditions of the experiment were rather artificial and unrealistic, despite the novel ideas they demonstrated. No player plods along hitting a single dinger each week, just as no one “saves up” all four of his homers to spit out once every four weeks in chunks of four.

Therefore, I thought a different type of experiment was in order. Let’s make it a bit more realistic and base the number of home runs on weekly team output. I decided to define a bunch of different teams – starting with consistent teams that always hit 9 home runs in a week, and add variation with teams that hit between 8 and 10, between 7 and 11, etc., until we get to a terribly inconsistent team who’s output in any week can range from 0 to 18.

Thanks to the magic of random.org, it is possible, even easy, to produce massive lists of random numbers within a range that you define. I made lists of 2,600 random numbers (100 Head to Head league seasons) according to each condition, pasted it into a spreadsheet, and then let the program do team by team comparisons for me.

What I ended up with is shown in the table below (I didn't run all the match ups, focusing primarily on teams E and F):
Team (HR range)
Avg. W vs A W vs B W vs C W vs D W vs E W vs F W vs G
Team A (9)
9 0 1213 1219 1228
Team B (8-10)
8.987 0 1215 1217
Team C (7-11)
9.01 0 1235 1204 1216
Team D (6-12)
8.999 0 1214 1221
Team E (0-18)
8.995 1246 1252
1235 1251 0 1220 1205
Team F (0-18)
9.12 1249 1245 1257 1252 1242 0 1253
Team G (3-15)
8.95 1170 1157 1240 1207 0




I can’t say I’m shocked with the results. I think this comparison shows that the effect of consistency is either insignificant – or left to random chance of match-ups. I see no real pattern in how teams perform against each other, so it is difficult to deduce any hard truths from the results, except that again, we see a seemingly insignificant or negative correlation with consistency. Below are a few comments on the results:
• Team F has a very high average (9.12) and performs very well against all comers, but you would expect that given that high average. Less easily explained is Team E’s performance. They did very well against most teams (almost as well as Team F), despite having a similar (if not lower) average than their opponents.
• Just to be clear – I think that if you simulated enough “weeks” the average would get closer to 9 for every team. It looks like 100 seasons may not have been enough to reach that point. But the similarity between the records of Teams E and F suggests that an inconsistent team may be better over the long haul.
• Team G performed poorly against teams A and C, but much better against team E. That’s one result that I don’t understand. It seems fluky. G does have the lowest overall average among these teams, so maybe that is the main reason for the wild discrepancy in team G’s performance against teams E and F.
• Even the biggest difference between our hypothetical teams is 59 “wins,” which is only one-half win per season. Both of those deficits were achieved by the G team – which coincidentally happened to have the lowest average of any of the teams.

I think that this experiment is as interesting as the previous one. Maybe I’m right and the distribution is no big deal. Or maybe my method remains too unrealistic. I might have to look around and figure out how to play around with what statisticians call a normal distribution, also known as a bell curve. That may be the best way to tackle this problem of inconsistency in head to head leagues.

But until then, I think we can say that it isn’t worth making a point of worrying about consistency. Now, Joe, it’s your turn to weigh in on this topic. Am I full of it? Should fantasy owners take consistency into account when building their teams? I still think that anyone who says so has a burden to prove that consistency can be predicted. I’m not so sure it is. Robinson Cano was pretty good in April of 2006, even though he started really slowly in 2007 and 2008. CC Sabathia has had solid Aprils as well as lousy ones.

In any case – even as a purely academic exercise, I think it’s been interesting. Readers are the true judges, however. Please get in touch with us – email us at baseballfaceoff@gmail.com, chat in the forums. We love it when our readers question us or comment on our articles. And if you have a question about another topic, please ask – we’re collecting good ones for our first-ever mailbag column.

All right, Joe, after some 3,000+ words, I guess it’s your turn.