Last week I described a way to factor out the level of luck a team has due to clustering of plays among innings and runs among games. I tried using four different approaches to estimating runs scored and allowed from atomic statistics like hits, walks, doubles, stolen bases, etc.:

- Runs Created (basic version)
- Runs Created (2002 version)
- Base Runs (basic version)
- Base Runs (detailed version)

and four approaches to estimating winning percentage from runs scored and allowed, all using the

Pythagorean approach with different exponents:

- constant 2
- constant 1.83
- Pythagenpat using team-average runs per game raised to the 0.287 power
- Pythagenpat using league-average runs per game raised to the 0.287 power

All sixteen combinations gave very similar results, so I used the basic version of Base Runs and the constant 1.83 exponent. I found that the Red Sox deserved to have far and away the best record in baseball and were quite unlucky to finish only 96-66, while the Diamondbacks were lucky to end up with a winning record, much less reach the playoffs. (I mined the raw statistics from

baseball-reference.com and wrote a program in

C to handle the calculations.)

Note that, unlike in some other statistical contexts, it's not necessary to correct for hitters' and pitchers' parks because for each team those cancel out. A team's stats and its opponents' stats are compiled in the same set of parks, so it's comparing apples to apples. But the results tell us only approximately how objectively good each team is; more exactly, they tell us how each team deserved to finish (with average luck) in the same season, against the same opponents. So if two teams have roughly the same Nostradamian record and one played in a tougher league or division, it's probably the better team.

This kind of approach works very well for teams but less well for individual players, as players with very high

OBA *and* SLG are slightly overrated by both Base Runs and Runs Created. The reason is that both measures estimate runs scored (or allowed) by effectively taking into account the probability of getting on base and the expected proportion of baserunners eventually scoring. When both are especially high for one player, the effect is unduly magnified. For example, the Rockies'

Matt Holliday excels at both getting on base and hitting baserunners in, but in real games he isn't hitting himself in, and most of the players around him in the lineup aren't as good as he is. Base Runs and Runs Created estimates how many runs a lineup with nine Matt Hollidays would score, but a better offensive measure of a single player might put him in a league-average lineup and measure how many runs he could be expected to add to that lineup. (Base Runs and Runs Created don't take into account a player's RBIs or runs scored because those stats depend heavily on the quality of the hitters ahead and behind him in the lineup.)

Also, the defensive stats measure the effectiveness of a team's pitching and fielding as a unit, which is ideal for projecting team records. There are better ways to evaluate individual pitchers' performances, as

Voros McCracken's revolutionary DIPS work showed. Essentially, he found that pitchers have much more control over their strikeouts, walks and home runs than they do over whether a ball put into play is an out or a hit, which depends more on the quality of the fielders behind him.

As for the

ALCS and NLCS matchups, the Red Sox (96-66) and Indians (96-66) and the Diamondbacks (90-72) and Rockies (90-73) may seem evenly matched, but the Red Sox deserved to finish more than 12 games ahead of the Indians and the Rockies deserved to finish more than 13 games ahead of the Diamondbacks. Still, don't forget

Joaquin Andujar's favorite word in English:

youneverknow.