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Offensive Volatility and Beating Win Expectancy

Armed with a new measure for offensive volatility (VOL), I wanted to revisit research I conducted  last year about the value of a consistent offense.

In general, the literature has suggested if you’re comparing two similar offenses, the more consistent offense is preferable throughout the season. The reason has to do with the potential advantages a team can gain when they don’t “waste runs” in blow-out victories. The more evenly a team can distribute their runs, the better than chances of winning more games.

I decided to take my new volatility (VOL) metric and apply it to team-level offense to see if it conformed to this general consensus*.

To determine a team’s overall offensive VOL, I used the same approach as I did with individual hitters — with two slight tweaks:

VOL = STD(RS/G)/Yearly_(RS/G)^.67

Where;

VOL = volatility

STD(RS/G) = the standard deviation of a team’s runs scored per game

Yearly_(RS/G)^.67 = a team’s seasonal runs scored per game, raised to the .67-power

The correlation between team VOL and the number of wins above or below their expected wins from 2002 to 2012** was -.34.

To get a better sense of the overall impact, I grouped teams into four buckets based on their VOL scores’ rankings (relative to other teams in each season) and then calculated the average wins above/below expected for each bucket. Here are the results:

VOL Rank Ave Wins +/- Expected Wins
Top 8 2
Upper-Mid 8 0
Lower-Mid 8 -1
Bottom 6 -2

Teams that ranked first through eighth in terms of VOL for a given season (where lower VOL equates to a more consistent offense) beat their expected win total by an average of two wins and were 1.6 times as likely to beat their expected wins than teams that finished outside of the top-eight in VOL for a season (64% vs. 40%). Compare that to teams ranked 25 through 30 and you have an overall difference of plus-four wins.

If we look at the top- and bottom-20 teams since 2002 — in terms of wins over/under expectations — the relationship is even clearer:

Year Team Actual Wins W-L% Over/Under Expected Wins VOL VOL Rank
2012 BAL 93 0.574 12 1.06 5
2008 LAA 100 0.617 12 1.02 3
2005 ARI 77 0.475 12 1.09 12
2004 NYY 101 0.623 12 1.18 25
2007 ARI 90 0.556 11 1.08 10
2004 CIN 76 0.469 10 1.04 5
2009 SEA 85 0.525 10 1.01 1
2007 SEA 88 0.543 9 1.08 11
2008 HOU 86 0.534 9 0.98 2
2010 HOU 76 0.469 8 1.09 8
2009 SDP 75 0.463 8 1.04 5
2002 MIN 94 0.584 8 1.15 20
2005 CHW 99 0.611 8 1.06 9
2006 OAK 93 0.574 7 1.04 2
2007 STL 78 0.481 7 1.23 28
2003 SFG 100 0.621 7 0.96 1
2002 OAK 103 0.636 7 1.05 4
2009 NYY 103 0.636 7 1.04 4
2003 CIN 69 0.426 7 1.11 11
2012 CIN 97 0.599 7 0.98 3
2007 BOS 96 0.593 -6 1.22 26
2011 HOU 56 0.346 -6 1.05 6
2006 TEX 80 0.494 -6 1.13 16
2009 WSN 59 0.364 -6 1.11 11
2007 SFG 71 0.438 -6 1.10 14
2005 NYM 83 0.512 -6 1.20 28
2006 ATL 79 0.488 -6 1.10 11
2008 ATL 72 0.444 -6 1.17 21
2005 SEA 69 0.426 -7 1.07 10
2008 TOR 86 0.531 -7 1.16 18
2011 KCR 71 0.438 -7 1.16 21
2003 HOU 87 0.537 -7 1.15 20
2009 CLE 65 0.401 -7 1.24 30
2002 BOS 93 0.574 -7 1.25 29
2004 DET 72 0.444 -7 1.15 21
2011 SDP 71 0.438 -8 1.30 29
2005 TOR 80 0.494 -8 1.09 13
2002 CHC 67 0.414 -8 1.21 27
2009 TOR 75 0.463 -9 1.13 15
2006 CLE 78 0.481 -11 1.19 26

The average VOL rank of the top-20 teams since 2002 was 8.5, with 14 of the 20 finishing in the top 10 for VOL. The bottom 20 came in at 19.6, with only two teams ranking in the top 10.

But how did teams fare in 2012 when it came to beating win expectations and the volatility of their lineups?

Here are the top- and bottom-five teams from this past season:

Team Actual Wins W-L% Expected Wins Over/Under Expected Wins VOL VOL Rank
BAL 93 0.574 82 12 1.06 5
CIN 97 0.599 91 7 0.98 3
SFG 94 0.580 88 6 1.09 10
CLE 68 0.420 64 5 1.16 19
WSN 98 0.605 96 3 1.07 7
TBR 90 0.556 95 -4 1.11 13
STL 88 0.543 93 -5 1.13 16
BOS 69 0.426 74 -4 1.17 23
ARI 81 0.500 86 -5 1.17 24
COL 64 0.395 69 -5 1.20 27

Baltimore, Cincinnati, San Francisco and Washington each ranked in the top 10 in terms of offensive consistency, while none of the five worst teams broke the top-10. The bottom three teams posted the 23rd, 24th and 27th ranked offenses when it came to VOL.

As has been stated previously, VOL isn’t a silver bullet. At the end of the day, a team’s success is mostly determined by it’s run differential. Putting together a highly consistent team at the sake of more run scoring doesn’t make sense.

To illustrate this point, I looked at teams with high- (1-8) and low-ranked (24-30) offenses and compared their average win totals based on whether those offenses where high-volatility (a VOL ranking of 24-30) or low-volatility (1-8):

Runs Scored Runs Scored
VOL High Low
High 85 70
Low 93 72

Poor offenses won about the same number of games, regardless of the volatility. But elite offenses won eight more games on average when they were also elite in terms of their consistency (93), compared to their highly inconsistent counterparts (85). (The results were similar when I compared poor and elite run-prevention teams.)

So what have learned so far?

First, it appears there’s difference between how players distribute their offensive performance throughout a season. (That has some relationship year-to-year.) Second, it seems the degree to which a team’s offensive production is consistent can have an impact on whether they can beat their expected record.

Both findings are still preliminary, but they suggest the next question: How does hitter volatility combine to determine overall run-scoring volatility? That’s a much trickier question, but I will hopefully have something on it in the near future.

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*I am still looking at the great feedback from colleagues and commenters about the new metric. For now, I decided to run this quick test with the existing metric.

**I derived expected wins using the Pythagenpat approach.