MLB Player Performance Index

For this project, I’m turning to the 2022 Lahman baseball database to develop an offensive score rating for players with over 4,000 at-bats. 

Note that this data is updated through the 2022 season. Future Hall of Famers such as Miguel Cabrara and Albert Pujols are still active during this time period and will only improve their overall score.

The process of creating an offensive score rating is quite an interesting one, although not always straightforward. It’s a great way to learn more about various players, compare them with others from their era, and maybe even get a sense of their Hall of Fame prospects. It’s a bit of a fun exercise that offers some neat insights into the world of baseball statistics.

How did I create my score?

1. To begin, I focused the dataset exclusively on position players who have recorded over 4,000 at-bats (removing players linked to steroid use), resulting in 1,233 players that we will rank.
2. Each player’s performance in various offensive categories was then evaluated and converted into percentiles to standardize comparisons.
3. I narrowed down the key categories to seven crucial ones: 1) home runs, 2) batting average, 3) runs batted in, 4) hits, 5) at-bats, 6) walks, and 7) strikeouts. I combined these percentiles for each player, applying different weights to each category based on their perceived significance in the game.

Here is an overview of the weighting system that I used.

Category	Weight
Home Runs	5
Average	5
Runs Batted In	4
Hits	4
At Bats	3
Base on Balls	2
Strike Outs	1

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Here is an example of how I computed the overall score for Babe Ruth.

Initially, I calculate where Babe Ruth ranks in percentiles among all hitters with more than 4,000 at-bats. Ruth excelled, landing in the highest percentile for home runs, batting average, runs batted in, and total at-bats. However, as is common with heavy hitters, he had a high strikeout rate, placing him in the 90th percentile for strikeouts, which impacts his overall rating negatively.

Player	Home Runs	Average	Runs Batted In	Hits	At Bats	Base on Balls	Strike Outs
Babe Ruth	1	1	1	4	9	1	90

I then subtract each metric from 100 (as we are used to seeing higher scores for better players) and apply the weighted metric as I described above. This gives us the following for Babe Ruth.

Player	Home Runs	Average	Runs Batted In	Hits	At Bats	Base on Balls	Strike Outs
Babe Ruth	495	495	396	384	273	198	10

The sum of these numbers is 2,251, the overall score we give Babe Ruth.

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My scoring system is designed to highlight players from the live ball era who demonstrated enduring excellence over their careers. It tends to favor those with lengthy, consistent careers, meaning a player who has sustained solid performance over 20 years is likely to rank higher than one who had a stellar career over a decade. Additionally, in an effort to maintain the integrity of the metrics, I’ve chosen to exclude players whose careers are associated with performance-enhancing drugs (PEDs).

The summary statistics on my dataset look like the following.

Maximum Score	2,315
Minimum Score	114
Range	2,201
Average	1,209
Standard Deviation	497

Overall, the first-place score goes to Stan Musical with a score of 2,315, and last place goes to Red Dooin, a catcher in the dead ball era, with a score of 114.

Reviewing the top ten players by offensive score, we see a list of all-time greats. My data is through 2022, including Miguel Cabrera, who retired in 2023 and could move up this list.

Rank	Score	Name	Position	Years Active	HR	AVG	RBI	H	AB	BB	SO
1	2315	Stan Musial	First Baseman	1941 – 1963	475	0.330	1,951	3,630	10,972	1,599	696
2	2261	Ted Williams	Left Fielder	1939 – 1960	521	0.344	1,839	2654	7,706	2,021	709
3	2260	Lou Gehrig	First Baseman	1923 – 1939	493	0.340	1,995	2721	8,001	1,508	790
4	2251	Babe Ruth	Right Fielder	1914 – 1935	714	0.342	2,217	2,873	8,398	2,062	1,330
5	2239	Mel Ott	Right Fielder	1926 – 1947	511	0.304	1,860	2,876	9,456	1,708	896
6	2238	Hank Aaron	Right Fielder	1954 – 1976	755	0.304	2,297	3,771	12,364	1,402	1,383
7	2226	Jimmie Foxx	First Baseman	1925 – 1945	534	0.325	1,922	2,646	8,134	1,452	1,311
8	2222	Willie Mays	Center Fielder	1951 – 1973	660	0.301	1,903	3,283	10,881	1,464	1,526
9	2221	Miguel Cabrera	First Baseman	2003 – 2022*	507	0.308	1,847	3,088	10,022	1,227	2,031
10	2220	Rogers Hornsby	Second Baseman	1915 – 1937	301	0.358	1,584	2,930	8,173	1,038	679

From my previous analysis that reviews offensive categories by year, we determined that runs are trending ever so slightly downward, and home runs and strikeouts are increasing. By analyzing Hall of Fame inductees by their offense score and their last year playing Major League baseball, we gain valuable insights into evolving standards for Hall of Fame selection. This approach allows us to assess whether the bar for induction is being raised over time, offering a unique perspective on the criteria and benchmarks that define a Hall of Fame-worthy career.

From the following scatter plot showing only Hall of Fame inductees, we get a correlation score of .40. Although not a large correlation, the offense output criteria to get elected appears to be increasing. Here, I have also limited the data set to Hall of Famers who are position players who are not pitchers or were elected for their managerial roles. This removed Joe Torre, Al Lopez, Billy Southworth, Charlie Comiskey, Wilbert Robinson, Casey Stengel, Miller Huggins, Bucky Harris, Ned Hanlon, and Leo Durocher, who all had over 4,000 at-bats during their career but were selected for the Hall of Fame due to their successful managerial careers.

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If we review all players, and not just Hall of Fame inductees, who had over 4,000 at-bats during their career, we do not see an increase in their offense score compared to the era in which they played.

This graph works best in proper visualization software, where you can hover over the data points to see which players are not inducted into the Hall of Fame but appear to have an offense score similar to their peers.

We can also look at the distribution of our offense score and see it does resemble a normal distribution.

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Let’s now look at players by position. 

Examining players by position offers us a unique lens through which to identify potential Hall of Fame candidates based on their career achievements. Additionally, this perspective sheds light on those who played in the dead ball era, a time when offensive accomplishments were more challenging to achieve. Recognizing the extraordinary feats of these players within the context of their era adds a valuable dimension to our appreciation of their skills and contributions to the game.

Catchers

Rank	Score	Player	HOF	Years Active	HR	AVG	RBI	H	AB	BB	SO
48	2,046	Mike Piazza	Y	1992 – 2007	427	0.307	1,335	2,127	6,911	759	1,113
69	2,006	Yogi Berra	Y	1946 – 1965	358	0.284	1,430	2,150	7,555	704	414
74	1,995	Ted Simmons	Y	1968 – 1988	248	0.284	1,389	2,472	8,680	855	694
77	1,988	Joe Torre	Y	1960 – 1977	252	0.297	1,185	2,342	7,874	779	1,094
102	1,943	Bill Dickey	Y	1928 – 1946	202	0.312	1,209	1,969	6,300	678	289
137	1,875	Gabby Hartnett	Y	1922 – 1941	236	0.297	1,179	1,912	6,432	703	697
158	1,847	Carlton Fisk	Y	1969 – 1993	376	0.269	1,330	2,356	8,756	849	1,386
184	1,803	Joe Mauer	N	2004 – 2018	143	0.306	923	2,123	6,930	939	1,034
191	1,788	Johnny Bench	Y	1967 – 1983	389	0.267	1,376	2,048	7,658	891	1,278
213	1,744	Ernie Lombardi	Y	1931 – 1947	190	0.306	990	1,792	5,855	430	262

First Baseman

Rank	Score	Player	HOF	Years Active	HR	AVG	RBI	H	AB	BB	SO
1	2,315	Stan Musial	Y	1941 – 1963	475	0.330	1,951	3,630	10,972	1,599	696
3	2,260	Lou Gehrig	Y	1923 – 1939	493	0.340	1,995	2,721	8,001	1,508	790
7	2,226	Jimmie Foxx	Y	1925 – 1945	534	0.325	1,922	2,646	8,134	1,452	1,311
9	2,221	Miguel Cabrera	N	2003 – 2022*	507	0.308	1,847	3,088	10,022	1,227	2,031
11	2,200	Albert Pujols	N	2001 – 2022*	703	0.296	2,218	3,384	11,421	1,373	1,404
20	2,164	Todd Helton	N	1997 – 2013	369	0.316	1,406	2,519	7,962	1,335	1,175
24	2,134	Eddie Murray	Y	1977 – 1997	504	0.287	1,917	3,255	11,336	1,333	1,516
30	2,106	Jeff Bagwell	Y	1991 – 2005	449	0.296	1,529	2,314	7,797	1,401	1,558
38	2,072	Johnny Mize	Y	1936 – 1953	359	0.312	1,337	2,011	6,443	856	524
40	2,067	Cap Anson	Y	1871 – 1897	97	0.334	2,075	3,435	10,281	984	330

Second Baseman

Rank	Score	Player	HOF	Years Active	HR	AVG	RBI	H	AB	BB	SO
10	2,220	Rogers Hornsby	Y	1915 – 1937	301	0.358	1,584	2,930	8,173	1,038	679
21	2,147	Charlie Gehringer	Y	1924 – 1942	184	0.320	1,427	2,839	8,860	1,186	372
42	2,066	Robinson Cano	N	2005 – 2022	335	0.300	1,306	2,639	8,773	620	1,214
49	2,043	Jeff Kent	N	1992 – 2008	377	0.289	1,518	2,461	8,498	801	1,522
62	2,023	Roberto Alomar	Y	1988 – 2004	210	0.300	1,134	2,724	9,073	1,032	1,140
79	1,986	Frankie Frisch	Y	1919 – 1937	105	0.316	1,244	2,880	9,112	728	272
82	1,980	Craig Biggio	Y	1988 – 2007	291	0.281	1,175	3,060	1,0876	1,160	1,753
101	1,943	Nap Lajoie	Y	1896 – 1916	82	0.338	1,599	3,243	9,590	516	321
103	1,941	Eddie Collins	Y	1906 – 1930	47	0.333	1,300	3,315	9,949	1,499	400
121	1,904	Ryne Sandberg	Y	1981 – 1997	282	0.284	1,061	2,386	8,385	761	1,260

Third Baseman

Rank	Score	Player	HOF	Years Active	HR	AVG	RBI	H	AB	BB	SO
12	2,199	George Brett	Y	1973 – 1993	317	0.304	1,596	3,154	10,349	1,096	908
13	2,188	Chipper Jones	Y	1993 – 2012	468	0.303	1,623	2,726	8,984	1,512	1,409
33	2,094	Adrian Beltre	N	1998 – 2018	477	0.286	1,707	3,166	11,068	848	1,732
84	1,975	Wade Boggs	Y	1982 – 1999	118	0.327	1,014	3,010	9,180	1,412	745
99	1,950	Aramis Ramirez	N	1998 – 2015	386	0.283	1,417	2,303	8,136	633	1,238
110	1,928	Ron Santo	Y	1960 – 1974	342	0.276	1,331	2,254	8,143	1,108	1,343
117	1,918	Eddie Mathews	Y	1952 – 1968	512	0.271	1,453	2,315	8,537	1,444	1,487
123	1,900	Ken Boyer	N	1955 – 1969	282	0.287	1,141	2,143	7,455	713	1,017
136	1,878	Scott Rolen	Y	1996 – 2012	316	0.280	1,287	2,077	7,398	899	1,410
139	1,875	Mike Schmidt	Y	1972 – 1989	548	0.267	1,595	2,234	8,352	1,507	1,883

Shortstop

Rank	Score	Player	HOF	Years Active	HR	AVG	RBI	H	AB	BB	SO
25	2,133	Derek Jeter	Y	1995 – 2014	260	0.309	1,311	3,465	11,195	1,082	1,840
61	2,025	Cal Ripken	Y	1981 – 2001	431	0.275	1,695	3,184	11,551	1,129	1,305
59	2,025	Honus Wagner	Y	1897 – 1917	101	0.327	1,733	3,420	10,439	963	735
65	2,014	Robin Yount	Y	1974 – 1993	251	0.285	1,406	3,142	11,008	966	1,350
71	2,001	Joe Cronin	Y	1926 – 1945	170	0.301	1,424	2,285	7,579	1,059	700
98	1,952	Julio Franco	N	1982 – 2007	173	0.298	1,194	2,586	8,677	917	1,341
118	1,915	Barry Larkin	Y	1986 – 2004	198	0.294	960	2,340	7,937	939	817
146	1,868	Michael Young	N	2000 – 2013	185	0.299	1,030	2,375	7,918	575	1,235
157	1,847	Alan Trammell	Y	1977 – 1996	185	0.285	1,003	2,365	8,288	850	874
161	1,845	George Davis	Y	1890 – 1909	73	0.294	1,440	2,665	9,045	874	613

Left Field

Rank	Score	Player	HOF	Years Active	HR	AVG	RBI	H	AB	BB	SO
2	2261	Ted Williams	Y	1939 – 1960	521	0.344	1839	2654	7706	2021	709
16	2170	Al Simmons	Y	1924 – 1944	307	0.334	1827	2927	8759	615	737
18	2166	Goose Goslin	Y	1921 – 1938	248	0.315	1609	2735	8656	949	585
26	2125	Carl Yastrzemski	Y	1961 – 1983	452	0.285	1844	3419	11988	1845	1393
27	2119	Billy Williams	Y	1959 – 1976	426	0.289	1475	2711	9350	1045	1046
43	2065	Jim Rice	Y	1974 – 1989	382	0.298	1451	2452	8225	670	1423
52	2037	Luis Gonzalez	N	1990 – 2008	354	0.282	1439	2591	9157	1155	1218
56	2029	Moises Alou	N	1990 – 2008	332	0.303	1287	2134	7037	737	894
76	1989	Bob Johnson	N	1933 – 1945	288	0.296	1283	2051	6920	1075	851
75	1989	Zack Wheat	Y	1909 – 1927	132	0.316	1248	2884	9106	650	572

Center Field

Rank	Score	Player	HOF	Years Active	HR	AVG	RBI	H	AB	BB	SO
8	2,222	Willie Mays	Y	1951 – 1973	660	0.301	1,903	3,283	10,881	1,464	1,526
22	2,147	Joe DiMaggio	Y	1936 – 1951	361	0.324	1,537	2,214	8,102	790	369
23	2,135	Mickey Mantle	Y	1951 – 1968	536	0.298	1,509	2,415	8,102	1,733	1,710
28	2,110	Tris Speaker	Y	1907 – 1928	117	0.344	1,529	3,514	10,195	1,381	323
34	2,093	Ty Cobb	Y	1905 – 1928	117	0.366	1,944	4,189	11,436	1,249	608
35	2,092	Ken Griffey	Y	1989 – 2010	630	0.283	1,836	2,781	9,801	1,312	1,779
54	2,033	Bernie Williams	N	1991 – 2006	287	0.296	1,257	2,336	7,869	1,069	1,212
57	2,027	Duke Snider	Y	1947 – 1964	407	0.295	1,333	2,116	7,161	971	1,237
58	2,026	Carlos Beltran	N	1998 – 2017	435	0.278	1,587	2,725	9,768	1,084	1,795
67	2,012	Al Oliver	N	1968 – 1985	219	0.303	1,326	2,743	9,049	535	756

Right Field

Rank	Score	Player	HOF	Years Active	HR	AVG	RBI	H	AB	BB	SO
4	2,251	Babe Ruth	Y	1914 – 1935	714	0.342	2,217	2,873	8,398	2,062	1,330
5	2,239	Mel Ott	Y	1926 – 1947	511	0.304	1,860	2,876	9,456	1,708	896
6	2,238	Hank Aaron	Y	1954 – 1976	755	0.304	2,297	3,771	12,364	1,402	1,383
14	2,187	Al Kaline	Y	1953 – 1974	399	0.297	1,583	3,007	10,116	1,277	1,020
15	2,171	Frank Robinson	Y	1956 – 1976	586	0.294	1,812	2,943	10,006	1,420	1,532
17	2,170	Vladimir Guerrero	Y	1996 – 2011	449	0.317	1,496	2,590	8,155	737	985
32	2,097	Harry Heilmann	Y	1914 – 1932	183	0.341	1,539	2,660	7,787	856	550
36	2,086	Dave Winfield	Y	1973 – 1995	465	0.282	1,833	3,110	11,003	1,216	1,686
39	2,069	Larry Walker	Y	1989 – 2005	383	0.312	1,311	2,160	6,907	913	1,231
41	2,066	Roberto Clemente	Y	1955 – 1972	240	0.317	1,305	3,000	9,454	621	1,230

Designated Hitter

Rank	Score	Player	HOF	Years Active	HR	AVG	RBI	H	AB	BB	SO
19	2,166	Frank Thomas	Y	1990 – 2008	521	0.301	1,704	2,468	8,199	1,667	1,397
29	2,109	Paul Molitor	Y	1978 – 1998	234	0.306	1,307	3,319	10,835	1,094	1,244
31	2,105	Harold Baines	Y	1980 – 2001	384	0.289	1,628	2,866	9,908	1,062	1,441
37	2,076	Edgar Martinez	Y	1987 – 2004	309	0.311	1,261	2,247	7,213	1,283	1,202
104	1,941	Victor Martinez	N	2002 – 2018	246	0.295	1,178	2,153	7,297	730	891
112	1,924	Chili Davis	N	1981 – 1999	350	0.274	1,372	2,380	8,673	1,194	1,698
171	1,820	Hal McRae	N	1968 – 1987	191	0.289	1,097	2,091	7,218	648	779
174	1,816	Nelson Cruz	N	2005 – 2022	459	0.274	1,302	2,018	7,358	732	1,870
220	1,735	Brian Downing	N	1973 – 1992	275	0.267	1,073	2,099	7,853	1,197	1,127
232	1,717	Don Baylor	N	1970 – 1988	338	0.260	1,276	2,135	8,198	805	1,069

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Summary

If we add up the top 10 scores for each position, we can get an idea of what positions have the most offensive players. 

Position	Score
First Base	21,765
Right Field	21,574
Left Field	20,950
Center Field	20,897
Second Base	20,253
Third Base	19,905
Shortstop	19,625
Catcher	19,035

Next, we can graph a cumulative summary between the First Base and Right Field positions to determine which position has the higher offensive output across all MLB players with over 4,000 at-bats. Reviewing the cumulative summary graph where the player rank is on the x-axis, we can see that First Baseman has a higher offensive output position, but is it statistically significant?

Placing the individual offense scores of First Baseman and Right Fielders into an online t-test calculator, we see no significant difference between the positions; as it states below, “By conventional criteria, this difference is considered to be not quite statistically significant.”.

The P-value is used to determine the statistical significance of the results from a hypothesis test. It represents the probability of observing the test results under the null hypothesis. In the context of a 99% confidence interval (CI), you would typically use a significance level (alpha) of 0.01.

In the t-test we performed, the P-value obtained was approximately 0.066. To determine if the result is statistically significant at the 99% confidence level:

• If the P-value is less than or equal to 0.01, the result is statistically significant, and the test passes the significance test at the 99% CI (meaning there is sufficient evidence to reject the null hypothesis at this level).
• If the P-value is greater than 0.01, the result is not statistically significant at the 99% CI, and the test fails the significance test at this level (meaning there is not enough evidence to reject the null hypothesis at this level).

Since the P-value (0.066) is greater than 0.01, the result is not statistically significant at the 99% confidence level. Therefore, the test fails to reject the null hypothesis at the 99% CI, indicating that any observed difference in performance scores could very well be due to random chance rather than a true difference between the groups.

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Conclusion

Evaluating Hall of Fame eligibility is far from a precise science. This exercise in devising my own evaluation system has been immensely engaging, involving countless hours of meticulous review, fine-tuning, and the discovery of players previously unknown to me.

My analysis leverages the comprehensive Lahman baseball dataset. Should you be interested in the code used for this analysis, please feel free to send me a message. Be aware, the data handling involved a significant amount of aggregation, pivoting, cleaning, and calculating. I’m more than happy to share my codebase with anyone interested in exploring it further.

Happy Coding!