The Relationship Between OPS Differential And Winning Percentage Using 5 Year Averages

See a recent post called The Relationship Between Team OPS Differential And Winning Percentage, By Decades. I used regression analysis to see how big the impact of OPS differential was on winning.

Here, instead of using individual years, I used the average OPS differential and average winning pct for all 30 teams over the last 5 years.

The regression equation from using individual years was

Pct = 1.325*OPSDIFF + .5

The r-squared was .827 and the standard error was .029. Over 162 games, that is 4.639 wins

The regression equation from using the 5 year average was

Pct = 1.3465*OPSDIFF + .5

The r-squared was .869 and the standard error was .017. Over 162 games, that is 2.72 wins. That is a big drop from the first regression. In a given year, luck will play a role. But the more seasons and data that are used the more accurate the relationship. By combining the years, some of the good and bad luck evens out.

The table below shows the prediction for each team. It seems strange the 6 most extreme teams are all pretty far from the rest of the pack. The Orioles were predicted to have a .476 pct but it was actually .505. That means they won 4.762 more games per season than their OPS differential would estimate.

Team	OPSDIFF	W-L%	Pred	Diff	Per 162
BAL	-0.018	0.505	0.476	0.029	4.762
PHI	-0.001	0.526	0.498	0.028	4.532
NYY	0.026	0.563	0.535	0.028	4.473
ATL	0.027	0.554	0.536	0.018	2.949
CLE	-0.021	0.487	0.472	0.014	2.349
MIN	-0.053	0.443	0.429	0.014	2.266
SFG	0.018	0.538	0.525	0.013	2.157
CIN	0.017	0.535	0.523	0.012	1.912
SDP	-0.023	0.481	0.470	0.012	1.899
PIT	-0.018	0.481	0.476	0.006	0.939
NYM	-0.024	0.473	0.467	0.006	0.931
KCR	-0.023	0.475	0.470	0.006	0.894
ARI	-0.020	0.475	0.473	0.002	0.371
STL	0.041	0.557	0.555	0.002	0.360
TOR	-0.009	0.489	0.487	0.002	0.251
LAA	0.023	0.532	0.531	0.001	0.149
WSN	0.024	0.530	0.533	-0.003	-0.415
SEA	-0.037	0.446	0.450	-0.004	-0.640
TBR	0.040	0.550	0.554	-0.004	-0.687
LAD	0.030	0.536	0.541	-0.004	-0.709
OAK	0.029	0.535	0.539	-0.005	-0.738
TEX	0.035	0.539	0.547	-0.008	-1.247
CHW	-0.008	0.479	0.489	-0.010	-1.642
MIL	0.016	0.509	0.522	-0.013	-2.115
HOU	-0.078	0.380	0.395	-0.015	-2.379
DET	0.050	0.552	0.567	-0.015	-2.446
FLA	-0.029	0.444	0.461	-0.016	-2.612
CHC	-0.032	0.427	0.458	-0.030	-4.918
BOS	0.034	0.514	0.546	-0.032	-5.231
COL	-0.017	0.444	0.478	-0.033	-5.404

Here is a graph of the relationship

The Statistical Dominance Of The 1927 Yankees

I recently listed The 25 Highest And Lowest Team OPS Differentials From 1914-2014. The 27 Yanks were number 1 by a good margin. Data from the Baseball Reference  Play Index and Retrosheet. I also regressed winning pct against OPS differential and got the following equation

Pct = 1.396*OPSDIFF + .500

Then I estimated every team's pct. Here are the top 10 project records

Team	Year	DIFF	Pred
NYY	1927	0.196	0.773
NYY	1939	0.158	0.720
ATL	1998	0.139	0.694
BAL	1969	0.136	0.690
NYY	1936	0.131	0.683
STL	1944	0.130	0.682
STL	1942	0.127	0.677
CLE	1948	0.127	0.677
NYY	1998	0.126	0.676
SEA	2001	0.126	0.676

Now the 27 Yanks actually had a .714 pct (why they did not reach .773 might be a good topic for a future post). But notice how big their lead is and how closely teams bunch up after the 1939 Yanks. The 27 Yanks would have an 8 game advantage over their 1939 counterparts in a 154 game season (although they would play each other so it might be a bit lower).

I also did the regression by decades. See The Relationship Between Team OPS Differential And Winning Percentage, By Decades. In some decades the impact of the differential was greater than others. But the 27 Yanks still dominate. Here is that top 10

Team	Year	DIFF	Pred
NYY	1927	0.196	0.769
NYY	1939	0.158	0.728
BAL	1969	0.136	0.698
STL	1944	0.130	0.697
STL	1942	0.127	0.692
CLE	1948	0.127	0.692
NYY	1936	0.131	0.689
NYY	1937	0.121	0.675
ATL	1998	0.139	0.673
STL	1943	0.114	0.672

Now OPS weights OBP and SLG equally. What if we give more weight to OBP? I used 1.7*OBP + SLG. Then I divided that by 3 since this approximates wOBA, a stat from Tangotiger. The regression equation in this case was

Pct = 3.34*wOBADIFF +  0.5

The 27 Yanks had a projected pct of .769, the 39 Yanks had .718, and the 69 Orioles had .694 and then the percentages slowly fall after that.

Now we don't have teams from 1901-13 since we don't know OPS allowed. But I did estimate pct using the differentials for the following 3 stats: HRs, Walks and non-HR hits. I was curious to see where the 1906 Cubs rank.

I also compared the estimated winning percentages for the 1914-19 teams from this method and the OPS differential method to see if they gave similar estimates. If they did, then it might be reasonable to project what the OPS differential would say for the 1901-13 teams based on the projection using these other 3 stats.

The good news is that the correlation between the percentages estimated by the two methods for the 1914-19 teams is .96. But the bad news is that there was one team for which the estimates differed by .048. That is pretty big.

But we can still get somewhere. The highest predicted winning pct for the 1901-13 teams was the 1902 Pirates with about .746. The 1906 Cubs were at .690 (why they actually had a .763 pct might make a good post, too).

For the Pirates to reach the .769 of the 27 Yanks, their estimate would have to go up about .023. But only 13 of the 96 teams from 1914-19 had their estimate from the OPS method exceed the 3 stat method by as much as .023. So it seems unlikely that the Pirates would catch they Yankees.

Also, of the 10 best actual winning percentages from 1901-13, only 1 other team had a prediction over .700, the 1905 Giants at .716.

Furthermore, of the 10 best actual 1914-19 teams, only 2 had their OPS differential prediction exceed their 3 stat prediction by at least .023. So it is unusual for a very good team to be off by much.

So it looks like only one team, the 1902 Pirates MIGHT come close to the 1927 Yankees. And that seems unlikely.

The Relationship Between Team OPS Differential And Winning Percentage, By Decades

I learned on Oct 21 that there are some discrepancies between Baseball Reference and Retrosheet, so I can't be sure of these results. If I learn more, I will report it.


Oct 24. Here are the corrected numbers:

Period	DIFF	INT	r squared	Std error	Per 162 Games
1914-19	1.866	0.498	0.833	0.038	6.207
1920-29	1.375	0.500	0.866	0.033	5.390
1930-39	1.442	0.500	0.851	0.038	6.157
1940-49	1.515	0.500	0.854	0.036	5.754
1950-59	1.452	0.500	0.874	0.032	5.165
1960-69	1.458	0.500	0.816	0.035	5.590
1970-79	1.361	0.500	0.811	0.032	5.165
1980-89	1.352	0.500	0.745	0.033	5.399
1990-99	1.249	0.500	0.780	0.032	5.109
2000-09	1.293	0.500	0.809	0.032	5.120
2010-14	1.325	0.500	0.827	0.029	4.639

Data from the Baseball Reference Play Index and Retrosheet.

DIFF is the value of the coefficient on OPS differential in the regression. INT is the intercept. Std error is the standard error. Per 162 games is the standard error times 162.

It seems like the relationship has gotten slightly stronger over time if you look at the standard errors, although the DIFF coefficient does not seem to be as strong as it used to be.

Also, for some reason, before the 1960s, the intercept was below .500. You might expect a team with a .000 OPS differential to have a .500 record. But that was not the case for some time. Not sure why. Maybe greater imbalance in talent levels across teams (like those great Yankee teams) meant that if you were just "average" you lost alot more than you would expect when you played those top teams.

Period	DIFF	INT	r squared	Std error	Per 162 Games
1914-19	1.898	0.429	0.802	0.042	6.759
1920-29	1.366	0.441	0.803	0.040	6.542
1930-39	1.548	0.423	0.822	0.042	6.807
1940-49	1.537	0.467	0.794	0.042	6.837
1950-59	1.486	0.494	0.858	0.034	5.490
1960-69	1.458	0.500	0.816	0.035	5.590
1970-79	1.361	0.500	0.811	0.032	5.165
1980-89	1.352	0.500	0.745	0.033	5.399
1990-99	1.249	0.500	0.780	0.032	5.109
2000-09	1.293	0.500	0.809	0.032	5.120
2010-14	1.325	0.500	0.827	0.029	4.639

The 25 Highest And Lowest Team OPS Differentials From 1914-2014

I learned on Oct 21 that there are some discrepancies between Baseball Reference and Retrosheet, so I can't be sure of these results. If I learn more, I will report it. 

Compiled from the Baseball Reference Play Index and Retrosheet

Oct. 24. Here are the corrected numbers:

Team	Year	OPS	OPSA	DIFF
NYY	1927	0.872	0.676	0.196
NYY	1939	0.825	0.667	0.158
ATL	1998	0.795	0.656	0.139
BAL	1969	0.756	0.620	0.136
NYY	1936	0.864	0.733	0.131
STL	1944	0.745	0.615	0.130
STL	1942	0.717	0.590	0.127
CLE	1948	0.792	0.665	0.127
NYY	1998	0.825	0.699	0.126
SEA	2001	0.805	0.679	0.126
PHA	1929	0.816	0.692	0.124
NYY	1937	0.825	0.704	0.121
CLE	1995	0.839	0.718	0.121
BRO	1953	0.840	0.722	0.118
CLE	1954	0.744	0.626	0.118
NYY	1932	0.830	0.714	0.116
NYY	1931	0.840	0.726	0.114
PHA	1928	0.799	0.685	0.114
ATL	1997	0.769	0.655	0.114
STL	1943	0.725	0.611	0.114
NYY	1921	0.838	0.725	0.113
BRO	1941	0.752	0.641	0.111
LAD	1974	0.743	0.633	0.110
PHA	1931	0.789	0.680	0.109
BOS	2003	0.851	0.742	0.109

Now the lowest

BOS	1927	0.677	0.796	-0.119
PHI	1945	0.633	0.752	-0.119
PIT	2010	0.678	0.797	-0.119
TOR	1979	0.673	0.793	-0.120
PIT	1952	0.631	0.752	-0.121
NYM	1965	0.604	0.728	-0.124
PIT	1953	0.676	0.803	-0.127
BOS	1932	0.665	0.792	-0.127
PHA	1919	0.634	0.761	-0.127
SLB	1937	0.747	0.875	-0.128
PHI	1939	0.669	0.797	-0.128
PHA	1920	0.642	0.771	-0.129
PHA	1915	0.615	0.745	-0.130
SLB	1951	0.674	0.804	-0.130
DET	1996	0.743	0.875	-0.132
PHA	1936	0.711	0.843	-0.132
SDP	1974	0.632	0.764	-0.132
FLA	1998	0.690	0.824	-0.134
DET	2003	0.675	0.813	-0.138
OAK	1979	0.648	0.786	-0.138
NYM	1963	0.600	0.739	-0.139
SLB	1939	0.720	0.860	-0.140
PHI	1928	0.716	0.857	-0.141
BSN	1924	0.633	0.776	-0.143
PHA	1954	0.648	0.804	-0.156

Here are the highest

Team	Year	OPS	OPSA	DIFF
NYY	1927	0.872	0.636	0.236
NYY	1939	0.825	0.638	0.187
NYY	1936	0.864	0.691	0.173
NYY	1931	0.840	0.673	0.167
NYY	1937	0.825	0.661	0.164
PHA	1929	0.816	0.655	0.161
PHA	1928	0.799	0.644	0.155
NYY	1921	0.838	0.683	0.155
NYY	1932	0.830	0.675	0.155
NYY	1930	0.872	0.719	0.153
SLB	1922	0.823	0.673	0.150
STL	1944	0.745	0.596	0.149
STL	1942	0.717	0.570	0.147
STL	1939	0.785	0.641	0.144
NYY	1926	0.806	0.663	0.143
NYY	1934	0.782	0.639	0.143
PHA	1931	0.789	0.648	0.141
NYY	1928	0.816	0.677	0.139
PHA	1930	0.821	0.682	0.139
ATL	1998	0.795	0.657	0.138
BAL	1969	0.756	0.620	0.136
NYY	1933	0.809	0.674	0.135
CLE	1920	0.793	0.659	0.134
STL	1943	0.725	0.592	0.133
WSH	1930	0.795	0.663	0.132

Now the lowest

Team	Year	OPS	OPSA	DIFF
SEA	1978	0.673	0.778	-0.105
SEA	1980	0.664	0.769	-0.105
KCA	1955	0.703	0.809	-0.106
KCR	2004	0.720	0.828	-0.108
KCR	2005	0.716	0.825	-0.109
MIN	2011	0.666	0.775	-0.109
SLB	1951	0.674	0.784	-0.110
NYM	1966	0.643	0.755	-0.112
KCA	1956	0.686	0.799	-0.113
SDP	1969	0.614	0.730	-0.116
TOR	1978	0.667	0.783	-0.116
TBD	2002	0.704	0.820	-0.116
NYM	1962	0.679	0.797	-0.118
HOU	2013	0.674	0.792	-0.118
DET	2002	0.679	0.798	-0.119
TOR	1979	0.673	0.793	-0.120
PIT	2010	0.678	0.798	-0.120
NYM	1965	0.604	0.728	-0.124
SDP	1974	0.632	0.764	-0.132
DET	1996	0.743	0.875	-0.132
FLA	1998	0.690	0.825	-0.135
DET	2003	0.675	0.813	-0.138
OAK	1979	0.648	0.786	-0.138
NYM	1963	0.600	0.740	-0.140
PHA	1954	0.648	0.803	-0.155

Team Winning Percentage As A Function Of OPS Differentials In High, Medium And Low Leverage Situations

I recently posted a regression generated equation where team winning pct was a function of overall OPS differential. It was based on the years 2010-14. All data from Baseball Reference's Play Index. Here it is

Pct = .5 + 1.3246*OPSDIFF

The r-squared was .827 and the standard error was .0286, which works out to 4.64 wins per season. I was interested in seeing how many more games the Royals won than their OPS differential of just .003 would indicate. It was 7.36.

Using the same years, here is the equation when breaking things down by leverage

Pct = .5 + .306*LOW +.420*MED + .564*HIGH

Where LOW, MED and HIGH are the OPS differentials in the three cases

The r-squared was .906 and the standard error was .0212, which works out to 3.44 wins per season. So a better estimate than just overall OPS differential.

Here are the PA percentages for each case in MLB in 2014

High) 0.205
Med) .365
Low) .43

So even though the high leverage situations are only around 20% of the total, they still have the biggest impact. Those are generally the cases where the game is closer and later than normal, usually with runners on base.

Here are the OPS and OPS allowed by the Royals for the three cases this year:

High) .713, .630
Med)  .713, .700
Low) .659, .700

Using those numbers to get the Royals' differentials and plugging thems into the 2nd equation we get a .540 pct, just a bit lower than their actual pct of .549. A .540 pct would give them 87.5 wins or just 1.5 fewer than expected. So their performance in high leverage situations for the most part explains how well they did this year. They move 5.86 wins closer to their actual total when leverage is taken into account.

See About WPA and Leverage.

Fangraphs on Leverage

How Have The Royals Won 7.36 More Games Than Their OPS Differential Would Indicate?

They had a .690 OPS during the season and allowed .687. That should give them a .50397 winning pct or 81.64 wins. They actually won 89 games. I had a regression about a week ago that had pct as

Pct = .5 + 1.3246*OPSDIFF

The tables below show what the Royals hit and allowed this year. Their big advantages are with RISP and when it is Late & Close. They had differentials of .052 and .057 in those two cases.

Royals	BA	OBP	SLG	OPS
Totals	0.263	0.314	0.376	0.690
None on	0.258	0.308	0.373	0.680
Men On	0.268	0.321	0.381	0.701
RISP	0.271	0.332	0.399	0.732
Late & Close	0.245	0.310	0.340	0.650

Royals Opponents	BA	OBP	SLG	OPS
Totals	0.250	0.310	0.377	0.687
None on	0.249	0.304	0.378	0.682
Men On	0.252	0.317	0.375	0.692
RISP	0.246	0.311	0.369	0.680
Late & Close	0.221	0.292	0.300	0.593

If I use some research I did a few years ago, Does Team Clutch Matter in Baseball?, where I estimate pct by breaking things down into RISP & NONRISP and Late&Close & NONLate&Close (the OPS and OPS allowed in each case), I get some slightly higher estimates for the Royals winning pct.

Using the Late&Close regression, they would have about a .520 winning pct and using the RISP regression, they would have about a .525 pct. There probably is a bit of an overlap between the two situations (maybe 4.165% because usually RISP is about 25% of PAs and L&C is about 16.66%-multiplying .25*.1666 gets about .04165).

But perhaps combining the two together would get us to about a .540 winning pct. That would be 87.5 wins and that is pretty close to the 89 they actually got.

Major League Situational Stats, 2010-2014

Compiled using the Baseball Reference Play Index.

Split	PA	BA	OBP	SLG	OPS
Total	923779	0.254	0.319	0.398	0.717
None On	520158	0.249	0.310	0.393	0.702
Men On	403621	0.261	0.332	0.405	0.737
RISP	238074	0.255	0.339	0.394	0.733
Late & Close	153559	0.240	0.316	0.365	0.681

Here is what I have for the years 1991-2000. The relative differences are not too much different than they used to be.

Is it hard to quantify the success of the Giants?

One of the announcers on the Fox broadcast said it was today (around the time they mentioned their low SB total of 55). They did win 2.67 more games than their OPS differential would predict. But that is not a very large difference. Even if we drop them from 88 to 85 wins, they still make the playoffs.

I looked at all teams from 2010-2014. In a regression, team winning pct was the dependent variable and OPS differential was the independent variable. Here is the equation

Pct = .5 + 1.3246*OPSDIFF

The Giants had a .699 OPS and allowed a .679 OPS for a .020 differential. Plugging that into the equation gives a pct of about .52649 while they actually had .54321. So their actual pct was about .0165 higher than predicted. Over 162 games that is 2.67 wins.

Could The Tigers Hit 200 HRs If They Played In Camden Yards?

They just said so on the TBS broadcast. Here are the road HR totals for all AL teams this year

Baltimore	104
LA Angels	82
Chicago Sox	81
Detroit	79
Toronto	79
Boston	74
Houston	73
Oakland	72
Cleveland	70
Tampa Bay	66
Seattle	63
Minnesota	61
Texas	60
NY Yankees	59
Kansas City	52

So the Tigers would have to hit 121 HRs in Camden. Here are the home HR totals for all AL teams this year

Baltimore	107
Toronto	98
Houston	90
NY Yankees	88
Detroit	76
Chicago Sox	74
Oakland	74
LA Angels	73
Seattle	73
Cleveland	72
Minnesota	67
Texas	51
Tampa Bay	51
Boston	49
Kansas City	43

So the Orioles out homered the Tigers in road games 104-79. So why would the Tigers out homer the Orioles 121-107 in Camden? They probably would not.

Also, over the 2011-13 seasons, Balt has had a 122 park index for HRs while Detroit has had 99 (just about average). So Balt allows about  22% HRs than average. But that is only half the games. So we could increase the Tigers HR total this year (155) by 11% and we would get 17 more HRs or 172. Far short of 200