The creation of Defense Independent Game Score (DIGS) and Batter Game Score (BaGS) happened the same way many creations do - with a series of rabbit holes and a lot of trial and error. As the names make obvious, both metrics are game scores, meaning they can be calculated to show how a player performed over any stretch of games desired. For many years, I’ve appreciated the simplicity of game score, which takes many different aspects of a performance and spits out a number that’s easy to compare to others. Though I don’t play fantasy baseball much anymore, I always preferred H2H points leagues, and I know that has played a role in the foundation of these metrics.
I started using game score (the Bill James version) about 10 years ago, first as a way to give pitchers custom W-L records based on score thresholds for each start. It was an enjoyable way to see which pitchers over history “should’ve” been 20-game winners, but were snubbed by poor run support. Eventually I ditched the original game score in favor of Tom Tango’s updated version (housed by Fangraphs), which was adjusted for each season, unlike the James version. That led me down more rabbit holes, like figuring out how to match scores to the park environment, scaling game scores to ERA, and calculating WAR. For someone who never took advanced math courses, these were huge accomplishments!
This was all happening at the time the baseball world was introducing us to Statcast, and a bevy of new metrics were coming onto the scene. A lot of focus was put into whether a metric was “descriptive or predictive,” and for the first time in my life I learned what a “root mean square error” was. I learned pretty quickly that game score could tell you the kind of year a pitcher had, but gave almost no help in predicting success in the next season. That’s when I set out to create my own more reliable pitching metric, DIGS.
DIGS has undergone many changes since I introduced it at the MLB level in 2019, but some things still hold true. Each score factors in IP, HR, BB, HBP, & K. Runs allowed have never factored into the equation. Originally, I only included batted ball events (BBE), while leaving out hits entirely. But that really capped the ceiling for each single game score, as pitchers who allowed few hits were never truly rewarded for it. So, hits are now part of the equation, and do play a big role on a single-game basis. Their cumulative role (think of the scores reported on in-season leaderboards) is much less, as both H & HR are regressed to conference averages. It’s like a sliding scale. Early on, the system defaults to league average results for batted balls. As the pitcher logs more batters faced during the year, though, he starts to get more credit for his batted ball outcomes. That’s an important piece of the puzzle. It’s also worth noting that players must meet a 2.0 IP/G minimum to acquire a DIGS score. This system isn’t designed for single-inning pitchers.
BaGS, on the other hand, gives a lot more credit to the player for the entirety of his contributions. The formula is designed with ottoneu fantasy scoring (and thus, wOBA components) in mind, with some tweaks here and there. It puts great emphasis on XBH and plate discipline, while also rewarding stolen bases. There is no regression for batted balls, but instead a small regression that keeps each player grounded toward the mean and covers for small sample craziness in the early-season.
Anyone can assign points to different events on a stat line and call it a metric, but where DIGS and BaGS stand out is in their adjustments. Every player in the nation gets rewarded or penalized for some different things (some of them out of their control, but each serving an important purpose), leading to the scores you see reported on the respective leaderboards. Scores are calculated so each conference is first “averaged out,” then given a boost or ding depending on overall strength. The first adjustment allows for players dominating their competition to stand out, while the second helps to lift those “power conference” players toward the top. Put it this way: The 25th best player in the SEC likely still gets drafted. The 25th best player in the SWAC doesn’t stand a chance. I try to control for that.
The other adjustments that occur for Division 1 players are for age and home park. DIGS scores are scaled to digsRA, BaGS to bOBA, then the park factor is applied to get that “+” score. The combination of player performance, conference strength, age, and park environment leads to a pretty powerful tool.
To say I have a specific goal for these metrics would be a little deceitful. Really, I want them to be relevant, reliable, and useful. That may look different for each user. Whether you’re looking for players to scout, players to pick up on a future fantasy team, or players to pick for season awards, both DIGS & BaGS can help provide a jumping off point for those decisions. My hope is whenever someone opens one of these leaderboards, be it current or past, they find a list that immediately passes the “sniff test” of reliability.
While I’m obviously biased, I hold firm to the belief that DIGS & BaGS work quite well. That they not only identify the most productive players, but also project players who will be coveted at the next level (be that JuCo to D1, D1 to MiLB, MiLB to MLB, and so on). For Division 1 hitting data going back to 2017, all hitters drafted in the first 20 rounds (nearly 600 of them) own a cumulative BaGS+ of 138. Hitters who weren’t drafted (or were taken after the 20th round) during the same period scored below average overall, by BaGS+.
“For Division 1 hitting data going back to 2017, all hitters drafted in the first 20 rounds (nearly 600 of them) own a cumulative BaGS+ of 138. ”
Division 1 DIGS leaderboards currently go back to 2018, and feature names like Logan Gilbert, Alek Manoah, Reid Detmers, Jack Leiter, Jared Shuster, Gavin Williams, Gordon Graceffo, & Nick Sandlin among the top 10 from the individual years 2018-2021. Many other respected prospects and current MLB pitchers litter the top 100’s for these lists, providing legitimacy to the collegiate DIGS leaderboards of today and tomorrow.
You should expect these leaderboards to update daily, with some behind the scenes work happening to keep conference ratings and overall averages updated each week. To say that I’m humbled by the opportunity to house my metrics with Prospects Live in a huge understatement. This is a site I’ve long looked up to, and one whose writers have always struck me as genuinely great people. People who care about promoting the game of baseball, about making it accessible to others, and about being transparent in their process. I’m honored to be associated with them. Enjoy the rabbit hole, and follow more of my work on Twitter at @DigsPitcher.
