Thus, we only have baseball WAR for position players from 2017 onward, as we were fortunate to obtain positions for the vast majority of players with significant playing time (> 150 ABs) across these years via D1Baseball’s stats page. These are difficult to obtain yet necessary for applying positional adjustments and estimating defensive runs saved. With position player WAR in baseball, perhaps the most burdensome limitation in our dataset was the lack of detailed fielding positions at the NCAA level. WAR = (Batting Runs Above Replacement + Positional Adjustment + Base-Running Runs + Fielding Runs ) / (Runs Per Win) In this blog, we will detail the steps involved in our calculations (based on Fangraphs’s methods detailed here for position players and here for pitchers), and then address the limitations of our version of WAR. ![]() Given the limitations of the metrics found on the NCAA’s website, we were able to calculate Pitcher WAR from 2013-2020 and Position Player WAR for those who cleared the requisite playing time threshold from 2017-2020. On the pitching side, our WAR is based on FIP rather than RA9, following Fangraphs’ methodology. On the hitting side, we use park-adjusted, as well as strength of schedule-adjusted (SOS), wOBA, estimated base-running runs (BsR estimate), estimated fielding runs (UZR estimate), and a positional adjustment for comparing different positions. We model our WAR for NCAA D1 players after Fangraphs WAR baseball calculations. The target threshold for D1 teams should be ~35-40 WAR per year.) (The visualization above shows the odds of making the NCAA tournament based on the cumulative WAR produced by each team from 2017-2019. ![]() ![]() Given the importance of WAR at the MLB level, we felt it was time to expand the same principles and methods to the college realm, as, to the best of our knowledge, calculations of NCAA WAR have not been attempted by any prior public analyst or organization. Integrating WAR in Baseball At The D1 Level For us, baseball WAR is a gateway to better analyses, better evaluations, and better comprehension of the sport itself. In this sense, although WAR is just a framework by design, it functions as much more than that for those around the game. In the past 2-3 years alone, WAR has influenced MVP and Cy Young races, helped sports writers rank and organize free agent lists, given fans a platform to evaluate the open market, and allowed analysts to determine how much a single draft selection is worth in present-day dollars. However, whatever your opinion is on the matter, it’s undeniable that in the year 2020, WAR in baseball has helped change the game forever. Certainly, like all metrics, baseball WAR is not perfect and it has its limitations depending upon whom you ask and what iteration you’re using. ![]() Wins Above Replacement Level, or WAR, is nothing more than a framework that attempts to summarize the entirety of a player’s contribution (fielding, baserunning, batting, or pitching) in one catch-all number. We are open to any feedback about the dashboard or questions about the process we went through in building WAR - covered here below and more informally on our latest DrivelineR&D podcast episode. Please contact on twitter or email if the dashboard is slow– we’ll try to respond in a timely manner.
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