How Many Games Do Clutch Hitters Really Win?

 This is an article I wrote for the "Beyond the Boxscore" site about 20 years ago.

This might seem like an easy question to answer. Just check how many times a player drove in the winning run in his team’s last AB (like David Ortiz, who seems to do it regularly). But very quickly problems arise. What about the guy who got on base and who scored the winning run? Should he get part of the credit? And what about times when a player fails to get a hit that would have won a game? Should that count as some kind of loss or be subtracted from the games he did win? None of these concerns even deal with whether or not players really can hit better than they normally do when the game is on the line. But I assume, for the sake of argument, that whatever difference in performance in the clutch that we find for players is real. Then I estimate how many wins (or losses) this brings to his team. 

First, I start with a a somewhat imprecise method and also a somewhat crude measure of clutch performance (since I am reposting an article from 20 years ago the next part will be a separate post soon using a stat like win probability added). The clutch stat is hitting in “close and late” or CL situations. These are situations when the game is in the 7th inning or later and the batting team is leading by one run, tied, or has the potential tying run on base, at bat or on deck. If a player hits better (or worse) in these situations, it will raise (or lower) his team’s overall performance. I use that increase (or decrease) to estimate the increase (or decrease) in wins over a full season due to a given hitter’s CL performance. 

Here is an example. Suppose a player has an OPS of .750 in non-CL situations (OPS is on-base percentage + slugging percentage). Let’s say he raises it by .072 when its CL (that is a pretty big increase). Spread over nine players in the lineup, it raises his team’s CL OPS by .008. But how many wins does that add over the course of a season?  For that, I will use a regression generated equation from some previous research I have done called “Does Team Clutch Matter?” A team’s winning percentage is estimate by 

(1) PCT = 0.501 + 0.918*NONCLOPS + 0.345*CLOPS - 0.845*OPPNONCLOPS - 0.421*OPPCLOPS

where CLOPS means how the team hit in CL situation and OPP refers to how their opponents did. NONCLOPS is how the team hit in non-CL situations. If a team’s CL OPS goes up by .008, its winning percentage rises by .345*.008 or .00276. Over 162 games, the team wins .447 more games (.00276*162). I then found all the players from 1987-2001 who had 6,000 or more plate appearances and found out how much their OPS went up or down in CL situations as compared to non-CL situations. Then I estimated how much their team OPS went up and then calculated its impact on team wins over a season. There was one additional step. I raised each player’s CL OPS by 4.5% because that is about the normal drop off in those situations (this might be due to not having the platoon advantage or just facing better than average pitchers when the game is on the line). If a player simply mainted his normal OPS win CL situations, he would essentially be a clutch hitter since OPS normally falls. 

The table below shows how the players ranked. 

Let’s take Tino Martinez. His non-CL OPS was .812 while his CL OPS was .889. But increasing that CL OPS by 4.5%, we get .929. This is .117 higher than normal and spread over 9 hitters in a lineup, it raises the team OPS when it is close and late by .013. How many wins does this add? We first multiply .013 by .345. This is .00449. That is the increase in team winning percentage. Then multiplying that by 162 we get about .727. So the best CL performer added .727 wins a year for his team. Also, there were only 5 players who added or subtracted even .5 or more wins (and well over half made a difference, whether positive or negative, of less than .25 wins per season).

So it is rare to find a player whose clutch performance will have much impact. That is something GMs should probably keep in mind when making personnel decisions. It is unlikely that clutch performance would be so different between two players that you would clearly pick one over the other. Even the biggest difference is only 1.286 (between Martinez and Carter). If two players were identical in every way, sure, you pick the better clutch player. But in most cases, there is not much difference. In fact, only about 20% of the differences between any two given players are .5 wins or more. If a GM was thinking about using clutch performance to help decide which player to sign, trade or trade for, it is not likelys it would matter much. And this assumes that these differences are real, not due to chance.

Name	CL OPS	CL OPS*1.045	NONCL OPS	Diff	Team Change	Win Value
Tino Martinez	0.889	0.929	0.812	0.117	0.013	0.727
Tony Gwynn	0.906	0.947	0.853	0.094	0.010	0.581
Gary Gaetti	0.782	0.817	0.732	0.085	0.009	0.530
Mark Grace	0.867	0.906	0.826	0.080	0.009	0.494
Tony Fernandez	0.765	0.799	0.746	0.054	0.006	0.334
Omar Vizquel	0.708	0.740	0.688	0.052	0.006	0.321
Luis Gonzalez	0.857	0.896	0.845	0.050	0.006	0.311
Dante Bichette	0.844	0.882	0.833	0.049	0.005	0.302
Harold Baines	0.842	0.880	0.831	0.048	0.005	0.301
Tim Raines	0.812	0.849	0.804	0.044	0.005	0.274
Edgar Martinez	0.951	0.994	0.956	0.038	0.004	0.235
Tony Phillips	0.783	0.818	0.781	0.037	0.004	0.229
Paul Molitor	0.848	0.887	0.850	0.037	0.004	0.228
Mark McLemore	0.691	0.722	0.688	0.034	0.004	0.210
Rickey Henderson	0.816	0.853	0.820	0.033	0.004	0.205
Cal Ripken	0.763	0.797	0.771	0.026	0.003	0.164
Ozzie Guillen	0.628	0.657	0.631	0.026	0.003	0.160
Brett Butler	0.752	0.785	0.760	0.026	0.003	0.159
Gary Sheffield	0.900	0.941	0.923	0.018	0.002	0.109
Wally Joyner	0.785	0.820	0.805	0.016	0.002	0.097
Mark McGwire	0.961	1.004	0.989	0.016	0.002	0.097
Jeff Bagwell	0.946	0.989	0.973	0.015	0.002	0.094
Delino DeShields	0.718	0.750	0.736	0.015	0.002	0.092
Jay Bell	0.746	0.780	0.767	0.013	0.001	0.078
Ruben Sierra	0.752	0.786	0.775	0.011	0.001	0.067
Andres Galarraga	0.835	0.873	0.862	0.010	0.001	0.063
Bobby Bonilla	0.815	0.852	0.842	0.010	0.001	0.059
Gregg Jefferies	0.744	0.777	0.769	0.008	0.001	0.051
Rafael Palmeiro	0.864	0.903	0.898	0.005	0.001	0.032
Terry Pendleton	0.705	0.737	0.733	0.004	0.000	0.026
Bernie Williams	0.858	0.897	0.893	0.004	0.000	0.022
Roberto Alomar	0.805	0.841	0.838	0.003	0.000	0.021
John Olerud	0.849	0.887	0.886	0.001	0.000	0.005
David Justice	0.852	0.890	0.891	0.000	0.000	-0.003
Barry Larkin	0.799	0.835	0.839	-0.004	0.000	-0.024
Chili Davis	0.799	0.835	0.843	-0.008	-0.001	-0.053
Barry Bonds	0.972	1.016	1.025	-0.009	-0.001	-0.056
Ken Griffey Jr.	0.902	0.943	0.952	-0.010	-0.001	-0.060
Fred McGriff	0.852	0.890	0.903	-0.013	-0.001	-0.080
Eric Karros	0.748	0.782	0.796	-0.014	-0.002	-0.087
Will Clark	0.840	0.878	0.893	-0.015	-0.002	-0.095
Steve Finley	0.731	0.764	0.779	-0.016	-0.002	-0.097
Marquis Grissom	0.682	0.713	0.731	-0.018	-0.002	-0.111
Ron Gant	0.760	0.794	0.813	-0.019	-0.002	-0.118
Benito Santiago	0.669	0.699	0.724	-0.025	-0.003	-0.155
Eddie Murray	0.754	0.788	0.816	-0.028	-0.003	-0.175
Devon White	0.688	0.719	0.748	-0.029	-0.003	-0.179
Jay Buhner	0.794	0.830	0.862	-0.033	-0.004	-0.203
Jose Canseco	0.816	0.853	0.886	-0.033	-0.004	-0.207
Paul O'Neill	0.774	0.809	0.843	-0.034	-0.004	-0.212
Kenny Lofton	0.741	0.774	0.812	-0.038	-0.004	-0.237
Dave Martinez	0.678	0.709	0.747	-0.039	-0.004	-0.242
Juan Gonzalez	0.847	0.885	0.924	-0.039	-0.004	-0.243
Robin Ventura	0.748	0.782	0.823	-0.041	-0.005	-0.254
Matt Williams	0.743	0.776	0.818	-0.041	-0.005	-0.257
Larry Walker	0.899	0.939	0.981	-0.041	-0.005	-0.258
Albert Belle	0.863	0.902	0.944	-0.042	-0.005	-0.260
Ray Lankford	0.783	0.818	0.863	-0.045	-0.005	-0.277
Todd Zeile	0.711	0.743	0.791	-0.048	-0.005	-0.298
Craig Biggio	0.747	0.781	0.831	-0.050	-0.006	-0.310
Sammy Sosa	0.811	0.847	0.900	-0.052	-0.006	-0.323
Brady Anderson	0.717	0.749	0.802	-0.052	-0.006	-0.325
Wade Boggs	0.759	0.793	0.853	-0.060	-0.007	-0.370
Chuck Knoblauch	0.713	0.745	0.806	-0.060	-0.007	-0.375
Ken Caminiti	0.714	0.746	0.809	-0.063	-0.007	-0.389
B.J. Surhoff	0.666	0.696	0.762	-0.066	-0.007	-0.409
Greg Vaughn	0.731	0.764	0.831	-0.067	-0.007	-0.419
Ellis Burks	0.785	0.820	0.890	-0.070	-0.008	-0.433
Travis Fryman	0.692	0.723	0.803	-0.080	-0.009	-0.497
Frank Thomas	0.905	0.946	1.034	-0.089	-0.010	-0.550
Joe Carter	0.665	0.695	0.785	-0.090	-0.010	-0.559