MAJOR LEAGUE EQUIVALENCIES
Major League Equivalents (MLEs) are a series of calculations designed to take non-major league baseball performance and estimate what that performanceâ€™s results would look like statistically in the context of the Major Leagues. Bill James gets credit for being the inventor of MLEs, as he outlined his method for batters in the 1985 Baseball Abstract. James was only interested at the time in making sense of minor league statistics, but MLEâ€™s theoretically can be used to evaluate ANY baseball performance, including minor league, Japanese or other foreign league, Negro League, NCAA league, etc. Not only those, but you can actually use the basic MLE procedure to evaluate the performance of an American League player relative to the National League, or vice versa (NLEs, ALEs?), or perhaps calculate what type of batting statistics Ty Cobbâ€™s 1909 performance would look like in the 2007 AL.
Of course, creating MLEs at all is a bit of a foolâ€™s errand. We canâ€™t really know how any player would have played in a different environment, especially one that is drastically different regarding its level of competition. Some players can adapt and adjust their playing when faced with different settings, and some have difficulty. Nevertheless, it can be a fun and enlightening exercise. For current baseball players, MLEs can also be used to build predictive models of future play results. This is really what James had in mind â€” a way to use past non-major league data to predict future major league performance of young players by converting that non-major league data into data that would approximate MLB level play, then use that data along with any MLB historical data on the player to give a greater sample size on which to make predictions. Today, everyone from major league team executives to fantasy league players rely on predictions built upon MLEs. Besides executives and fantasy baseball players, MLEs can be useful for baseball historians and baseball â€˜gamersâ€™ (those who play simulation games like Diamond Mind, Out of the Park, APBA, and others). MLEs can help to answer questions such as:
- How would Ted Williams, Bob Gibson, Ty Cobb, and Barry Bonds do if all played in a league together?
- What if Japanese League players had been allowed in MLB beginning in the 1960â€™s?
- What if the Major Leagues had integrated in the 1920â€™s?
MLEs can give us somewhat realistic â€œWhat ifs?â€ that can be analyzed, simulated, and just enjoyed. Creating MLEâ€™s generally involves these steps:
1. Determine the relative strengths between the FROM League environment and the TO League Environment.
Ideally, youâ€™d have actual data from which to do this, such as players who move from PCL to NL within the same year. Compare their stats between the two, adjust for quantity (player may have only 5 plate appearances in NL and 450 in PCL that year, while another has 400 and 70, for example), sum up, and compare. For Japanese Leagues, you generally only have players moving to and from MLB BETWEEN seasons, so you would want to pair one season to the following season, but since the player would be a year older the 2nd year, you have to make a slight adjustment for age to make those pairs comparable. For Negro Leagues, you may have only limited pairs in the 1940â€™s, or NO pairs in the 1920â€™s, in which case you have to possibly make some big assumptions (guesses) about league strengths.
2. Determine the differences in League Run Environments.
This SHOULD be straightforward, but itâ€™s not. For example, if 10 Runs per Game are scored in the PCL, and 8 Runs per Game are scored in the NL, you would think that the PCL stats for the MLE calculation would need to be decreased by 20% for batters and pitchers (lower runs allowed for pitchers). However, ballparks on average may be a little smaller in the PCL, and perhaps if the PCL had played the season in MLB parks, they would have scored only 15% more than the NL instead of 25% more. If thatâ€™s the case, then a batter moving from the PCL to the NL is going to see his offensive numbers decline by even MORE than 20%, while a PCL pitcher would actually see his Runs Allowed improve by more than 20%! Which means you need the next step:
3. Determine the differences in Ballparks between leagues.
As mentioned, league run environments are impacted by the parks. If a player like Tuffy Rhodes is moving from the NL to Japan, heâ€™s moving to a run scoring environment around 6% LESS than MLB, so we would expect his stat line adjustment in Step #2 to be 6% worse. However, parks in Japan are much more hitter friendly ON AVERAGE than parks in MLB, perhaps as much as 13% more hitter friendly. So, not only does Tuffy get around a 10% boost in step #1 for moving to a weaker league, he gets another 7% boost from steps #2 and #3 together. Calculating this step is tricky, because the evidence is intertwined with the league run scoring environment. The best estimating technique is to look at the DIFFERENCE between batters and pitchers who move between the same league environments. For example, if the empirical evidence shows that PCL batters hit 15% worse in MLB, while PCL pitchers allow only 5% more runs moving to the MLB, thatâ€™s evidence that the PCL parks are around 5% more hitter friendly on average than MLB parks.
4. Determine the differences in Ballparks WITHIN leagues.
Step #3 uses the â€˜averageâ€™ parks for the FROM and TO leagues, but obviously the specific park a batter played in, and the specific park heâ€™s being calculated into, also have to be adjusted for. There have been several good publicly available methods already created to calculate MLEâ€™s.
Bill James of course had his formulas in the 1985 Abstract, specifically for AA and AAA players going to MLB. James then had the â€œWillie Davis Methodâ€ in his Historical Baseball Abstract, specifically to MLE any one major league batting season into a â€˜neutralâ€™ major league. Dan Syzmborski does MLEs that are calculated very similar to Bill James for batters, only he also has formulas for pitchers.
Seamheads.com has already published some Japanese player MLEs in an earlier article, and in the future weâ€™ll be publishing Negro League MLEs that various analysts have developed.Â In the spring, weâ€™ll probably publish MLEs for some of the significant rookies of 2008.
In the meantime, weâ€™re providing a spreadsheet that our readers can use to play around with creating their own MLEs. The batting MLEs in this sheet are based primarily on the â€œOdds Ratioâ€ method outlined in many blogs over the years by Tom M. Tango of â€œThe Book.” The pitching MLEs have gone through many different methodologies, as not having doubles and triples allowed for most pitchers creates challenges. The pitching MLE calculations currently follow closely the method of Justin Kubakto, used by Baseball-Reference.com for their neutral stat calculations.
While this is just provided as a fun tool to give an approximation MLE, we do welcome any feedback to make the MLE calculations more accurate.