Written by: Luke Smailes

We at 643 Charts are excited to bring Defensive Runs Saved (DRS) to college baseball and softball for the first time. Our main development focuses this summer have been centered around advancing the collegiate market’s access to tools to better evaluate, develop, and deploy defenders. This includes coaches, analysts, and media members.

The metric, originally developed by The Fielding Bible for Major League Baseball, captures a player’s total defensive value, and displays it as the amount of runs that the player saved/cost his or her team compared to the respective league’s average defender at that position. With positional adjustments, we can also compare players across positions and determine how much value overall they created/lost with their play on defense. DRS, along with Ultimate Zone Rating (UZR) and Outs Above Average (OAA), are the main publicly available defensive metrics leveraged at the MLB level that influence roster decisions, lineup deployments, and postseason awards.

The collegiate market is currently forced to base defensive decisions on standard stats. Fielding percentage, putouts, assists, generalized defensive efficiency estimates at the player level, or even simply the eye test are all used by programs to make defensive decisions, but none of these methods tell a comprehensive story. We recognized a demand for advancements in defensive analysis in the collegiate space, so to start that, we are calculating and implementing Defensive Runs Saved. DRS represents the most comprehensive defensive story told in a single metric. This blog seeks to provide a high-level overview of the metric’s construction based on the data at our disposal and also provide some results that display the nation’s top defenders.

With our access to the baseball and softball APIs from Synergy Sports through our strategic partnership, we’re able to access tagged data that allows us to calculate our somewhat simplified version of DRS. Synergy’s in-depth tagging system, that scales across the entirety of D1 baseball and softball, provides us a rich dataset that allows us to confidently bucket balls in play and develop probabilities of plays being made. The metric is an accumulation of different calculations for the different position groups, which are infielders, outfielders, and catchers with positional adjustments applied for each specific position.


DRS for infielders is comprised of:

  • Ground Ball Runs Saved
  • Double Play Runs Saved – Start
  • Double Play Runs Saved – Pivot
  • Infield Fly Ball Runs Saved
  • Bunt Runs Saved

Ground Ball Runs Saved is the foundational component of DRS for infielders. It leverages the batted ball location, quality of contact, whether first base is occupied – and if it is, whether the runner was attempting a steal prior to the ball being hit into play. All these factors influence the expected outcome of the ground ball. If the play is made, the defender is rewarded with 1 minus the probability that the play “should have been” made based on what other defenders at that position have done historically on similar (based on the factors listed above) batted balls. That value is then multiplied by the expected number of bases saved. For most ground balls on the infield, there’s one base (or slightly more than one base) at stake on average. However, if a third baseman makes a play on a hard hit ball to his or her backhand, they’re probably saving more than one base, and this is accounted for in the model. Conversely, if the play is not made, this procedure is simply flipped so that the defender is penalized by the probability that the play was made multiplied by the expected bases saved.

This is about as “in the weeds” as I’ll go, and if this sounds confusing, know that the calculation is very straightforward for the most part. It’s the explanation of the nuts and bolts that make it sound more confounding than it actually is. This example should help:

If a shortstop is attempting to make a play on a given ground ball to his right that has just a 20% chance of turning into an out on average (i.e., a very tough play), he’ll get a lot of credit (0.8) for making the play, and only slightly debited if he doesn’t. If the situation is flipped and that batted ball is turned into an out 80% of the time by the average D1 shortstop, then he’ll only get a little bit of credit (0.2) for making the play that most everyone else does too, but if he boots the ball, or doesn’t get the out for whatever reason, he’ll be penalized quite a bit.

Here are the Ground Balls Runs Saved leaders for both sports in 2023:

Rank Player Pos School Ground Ball Runs Saved
1 Simon Scherry SS Evansville 12.2
2 Nik McClaughry SS Arizona 10.7
3 Anthony Silva SS TCU 10.7
4 Benjamin Blackwell SS Clemson 10.4
5 JJ Freeman SS Delaware 10.4

Evansville’s Simon Scherry has a decent-sized lead over second place for a team that ranked 23rd in D1 in Defensive Efficiency.

Rank Player Pos School Ground Ball Runs Saved
1 TJ Webster SS Western Kentucky 6.6
2 Sara Vanderford 3B Texas State 6.5
3 Jasmine Williams SS UCF 6.4
4 Charla Echols 3B Florida 6.3
5 Brooke Benson SS Indiana 6.2

For softball, Western Kentucky’s shortstop, TJ Webster, takes home the Ground Ball Runs Saved crown. Softball values will consistently be smaller when compared to baseball for a couple of reasons. First, since softball games are shorter; there’s not as many outs to get. Also, there’s more runs scored per inning in baseball, and this means that the stakes (in terms of runs saved/lost) of each defensive opportunity are lower in softball.

The other components, for all positions, not just infielders, follow the same credit/debit (or in other words, plus-minus) system. For infielders, the next component is Double Play Runs Saved which is split into plays where the defender was the first touch on a double play opportunity (Start) and plays where they were the second touch (Pivot).

The last two components are Infield Fly Ball Runs Saved, which is exactly what it sounds like, and Bunt Runs Saved, which measures a player’s ability to convert outs on bunted balls.

Here are the overall infield DRS leaders:

Rank Player Pos School INF GB DP-Start DP-Pivot IFFB Bunt DRS
1 Simon Scherry SS Evansville 12.2 0.9 0.4 -0.7 0 12.7
2 Benjamin Blackwell SS Clemson 10.4 1.6 0.4 0.1 0 12.5
3 Nik McClaughry SS Arizona 10.7 1.0 0.3 0.1 0 12.1
4 JJ Freeman SS Delaware 10.4 1.4 0.2 0.1 0 12.0
5 Kevin Keister 2B Maryland 10.0 1.4 0.5 0.1 0 12.0
Rank Player Pos School INF GB DP-Start DP-Pivot IFFB Bunt DRS
1 Sara Vanderford 3B Texas State 6.5 0.5 0 0.18 1.8 8.9
2 Charla Echols 3B Florida 6.3 0.6 0 0 1.4 8.3
3 Jasmine Williams SS UCF 6.4 0.8 0.2 0.1 0 7.5
4 Savana Sikes 3B Ole Miss 2.9 0.7 0 0.1 3.2 6.9
5 Camryn Michallas 3B Marshall 5.2 0.1 0 0.1 1.6 6.9

What’s immediately evident here is that because of the higher usage of short game strategies on offense in softball, having a good defender at the hot corner is more important. Texas State’s Sara Vanderford was just behind Webster in runs saved on ground balls, and she accumulated value is various ways as shown here:

Ole Miss’ Savana Sikes led the nation in Bunt Runs Saved, and as we can see here, she made several very athletic plays to cut down lead runners.


DRS for outfielders is comprised of:

  • Fly Ball Runs Saved
  • Arm Runs Saved

The composition of DRS for outfielders is much simpler, consisting of Fly Ball Runs Saved, which measures the defender’s ability to balls in the air to the outfield into outs, and Arm Runs Saved, which essentially serves a bonus for outfield assists.

Like the calculation for infielders, Fly Ball Runs Saved starts by factoring in the batted ball location and the quality of contact, but what’s added is the batted ball type (a good proxy for trajectory and hang time), along with the batted ball’s distance from home plate. These four components give us the expected catch probability for each fly ball/line drive to the outfield.

 Here’s the leaders:

Rank Player Pos School Flyball Runs Saved Arm Runs Saved DRS
1 Enrique Bradfield Jr. CF Vanderbilt 12.5 0.4 13
2 Austin Overn CF USC 10.7 0.7 11.4
3 Vance Honeycutt CF North Carolina 9.1 1.3 10.4
4 Colton Ledbetter CF Mississippi St. 9.7 0.3 10.0
5 Isaac Humphrey RF Louisville 5.9 3.7 9.6

It’s likely not a surprise to any college baseball fans that Bradfield Jr, the 17th overall pick in July’s MLB draft by Baltimore, is leading the way here, and his landing at the top was frankly a welcome sign for the outfield portion of the metric.

The softball leaderboard features McKenzie Clark, who was the total package roaming centerfield for Clemson, and Ciara Briggs, a two-time Rawlings Gold Glove winner for LSU. However, it was Texas State’s Piper Randolph that led D1 Softball in Fly Ball Runs Saved in 2023.

Rank Player Pos School Flyball Runs Saved Arm Runs Saved DRS
1 McKenzie Clark CF Clemson 4.8 2.9 7.7
2 Ciara Briggs CF LSU 5.5 1.2 6.7
3 Aaliyah White LF Incarnate Word 2.2 4.0 6.2
4 Piper Randolph CF Texas State 6.1 0 6.1
5 Skyler Shellmyer CF Northwestern 5.3 0.8 6.1

Piper Randolph tracks down fly balls in the outfield for Texas State

We decided to not attempt to calculate expected Arm Runs Saved, as there are too many factors that dictate whether the outfielder “should have” thrown out a runner for us to reliably compare a defender to league average for a given throwing situation. A good example of this is the myriad of factors that influence whether an outfielder attempts to throw out a runner at the plate. The runner’s speed, the score of the game, the base-out state, etc. all influence the decision-making process of the outfielders, and attempting to factor in these attributes of the situation creates samples that are overly granular.

To calculate Arm Runs Saved, we take all plays where a throw from an outfielder resulted in a runner being either doubled off or thrown out trying to advance. The credit given to the outfielder becomes the difference between the expected runs of the resulting base/out state and the expected runs of the base/out state had the runner not been thrown out.


DRS for catchers is comprised of:

  • Framing Runs Saved
  • Stolen Base Runs Saved
  • Pickoff Runs Saved

For catchers, our version of DRS compares each player to league average in terms of pitch framing and controlling the opposing team’s running game. Framing Runs Saved follows the same pitch framing model used to calculate Framing+ in our interface. In short, the result of each pitch that a catcher receives is compared to the league average result of pitches like it based on the count and location of the pitch. The effect of catchers who consistently win borderline pitches can be as much as a full win over the course of a season, or in the case of Kyle Teel for Virginia last season, about a win and a half for the Cavaliers.

Stolen Base Runs Saved compares a catcher’s ability to cut down would-be base stealers to the league average catcher, and for baseball, we followed The Fielding Bible’s method of giving catchers 35% of the responsibility of the outcome of a stolen base attempt, with the pitcher getting the other 65% of credit/blame. For softball, catchers are given 100% of the credit for throwing out a runner because there’s no responsibility for the pitcher to hold the runner and the pitcher’s time to the plate doesn’t affect the time the catcher has to throw out the attempting base stealer.

For catcher Pickoff Runs Saved, catchers effectively get a bonus for picking off runners, which is similar to Outfield Arm Runs Saved in that regard. However, they can also be penalized for the resulting run value of pickoff attempts that result in the runner advancing.

Let’s take a look at the leaders behind the plate.

Rank Player Pos School Framing Runs Saved Throwing Runs Saved DRS
1 Kyle Teel C Virginia 16.0 1.4 17.4
2 Cameron Gill C Wofford 12.4 0.7 13.2
3 Jacob Cozart C NC State 12.1 0.6 12.7
4 Darius Perry C UCLA 10.0 2.0 12.0
5 Manny Garza C Rice 9.1 1.9 11.0

Virginia’s Teel, the 14th overall pick by the Boston Red Sox in July’s MLB draft, was more than four runs better than the next best catcher.

Throwing Runs Saved is composed of Stolen Base Runs and Pickoff Runs, and this structure allows us to isolate individual components that catchers excel at. An interesting nugget came from Sam Houston State’s Walker Janek who led the nation in Throwing Runs Saved with 3.2, but he did so not by cutting down base stealers, but by saving 3 runs on pickoffs. Janek back-picked ten runners in 2023 to lead D1.

Rank Player Pos School Framing Runs Saved Throwing Runs Saved DRS
1 Aly Kaneshiro C Stanford 9.1 4.9 14.0
2 Ally Shipman C Alabama 7.4 2.3 9.7
3 Grace Kilday C UC Davis 5.0 4.0 9.0
4 Sharlize Palacios C UCLA 6.7 1.6 8.3
5 Carlli Kloss C Notre Dame 3.5 4.3 7.8

Stanford’s Aly Kaneshiro led the way in softball by a wide margin with 14 runs saved. She had more Framing Runs saved than any other catcher in D1 softball, and only Utah State’s Makenzie Macfarlane had more Throwing Runs Saved (5.8) than Kaneshiro’s 4.9.

We feel like this metric represents a significant step forward in defensive analysis in college baseball and softball, but we also realize that it’s not perfect. For example, DRS does not account for defensive positioning, so coaches that optimally position their defenders will see them accumulate higher DRS numbers. Here’s a good example of Piper Randolph flagging down a hard-hit ball in the left-center gap that was not necessarily a “routine” play, yet we can see that her being positioned on the left side of second base pre-pitch made it a lot easier.

A metric like MLB’s Outs Above Average controls for positioning, and other factors like runner speed, for example, that DRS does not leverage to determine the probability of an out being made on a given batted ball. 

However, with TrackMan now featuring defensive positioning outputs that are being fed into our data mapping infrastructure, we’re also in the process of controlling for some of those factors to construct our own OAA-like metric, as well as shift recommendations for clients based on the components that make up these metrics. 

As for what’s next for Defensive Runs Saved, we now have a defensive component that can be added to our Weighted Wins Above Average (wWAA) metric. wWAA, a metric we debuted last Spring, currently stacks players up against their league’s average player. However, Wins Above Replacement (WAR) is what’s been popularized in MLB, and it conveys the number of wins a player contributed to his or her team above the theoretical “replacement”-level player. In MLB, that’s something like a Triple-A minor league player, so constructing exactly what that replacement level produces at the college level is our next task. This will allow us to create the first comprehensive WAR metric for college baseball and softball that includes a defensive component. 

For now, 643 Synergy Plus or Premium clients can access a full DRS leaderboard under the ‘Defense” tab in the Synergy tile.

Follow us on Twitter (X) @643charts for plenty of content surrounding DRS and the release of our new WAR metric in the coming weeks!