Written By: Forrest Allen, 6-4-3 Charts Data Scientist
Introduction
As technology has proliferated to college baseball stadiums, these programs now have access to data never seen at the college level. Using this new data from our partners at Trackman, 6-4-3 Charts has created an Outs Above Average (OAA) metric to measure and quantify players’ defensive ability. What follows is an explanation of how to interact with this metric in our interface and how play probabilities are generated.
Outs Above Average in the 6-4-3 Charts Interface
The 6-4-3 Charts interface delivers accurate, comprehensive data through intuitive, interactive tools that help users evaluate players and make better decisions. The OAA tab in the Trackman Defense tile is no different. The tab is set up to investigate a single defender. Upon selecting a name, all his opportunities at all the positions he played across his career. As with everything in the interface, the data can be filtered in a variety of ways to give each user the customized view they need.
Spray Chart
The spray chart provides a “map” of where a defender’s balls in play landed. They are color coded by difficulty. In addition, by hovering over each batted ball event (BBE), you will see pertinent data such as exit velocity, bearing, cover distance, hang time, out probability, difficulty, and whether the play was made. Speaking of pertinent data, now is probably a good time to explain how OAA works, and thereby why this data matters. Soon, where available, Synergy video will be linked throughout the tool allowing users to box select plays from the spray chart or click to view video via the data table.
Example Infield Spray Chart from the 643 Charts Trackman Baseball Outs Above Average Tab – FSU’s Alex Lodise
Example Outfield Spray Chart from the 643 Charts Trackman Baseball Outs Above Average Tab -UConn’s Caleb Shpur
At a high level, OAA assigns an out probability to a BBE. Every BBE either results in a credit or debit for a given defender. BBEs converted to outs result in a credit while failing to make the play results in a debit. The magnitude of this credit or debit is proportional to the difficulty of the play. Routine BBEs give less credit for conversion to outs and steeper penalties for failure to do so. Difficult BBEs are the opposite; smaller penalties for not converting to an out and more credit for making the play. The sum of these credits and debits is a player’s OAA score.
Out Probabilities
Two primary factors influence the difficulty of converting a BBE into an out.
- The amount of time a defender has to make the play
- Measured by exit velocity for infielders
- Measured by hangtime for outfielders
- The distance a defender must cover to make the play
- Measured by distance between a fielder’s starting position and a BBE’s landing location for outfielders
- Measured by the difference between the bearing of the BBE and the fielder’s bearing for infielders.
For each combination of distance and time at each position, we simply calculate the number of outs made and divide by the number of occurrences to determine the out probability. To demonstrate how this works in practice, we can look at center field. Of the more than 80,000 BBEs to center field, 344 have a hang time between 2.75 and 3 seconds and required the defender to travel between 30 and 40 feet. 281 of these BBEs were converted to outs, making the out probability 82% (281/344). We repeat this same process for 11 distance intervals (10 feet) and 13 hang time intervals (.25 seconds) for each position, producing 143 possible combinations and allowing us to assign a probability to each BBE.
Bearing as Distance
I mentioned earlier that we use the difference in bearing between a fielder and a BBE to calculate how far an infielder must range to make a play. Batted ball bearing, also known as spray angle, is the angle at which the ball comes off the bat and ranges from -45 to 45 degrees. A bearing of 0 would be a BBE directly over second base. For the defender, we can derive his bearing from Trackman data using trigonometric functions that I won’t go into here. To find the difference between the two, simply subtract batted ball bearing from fielder bearing to determine how far an infielder needs to range to make a play.
Batted Ball Bearing Example
Using this paradigm, a BBE with a bearing of -20 (the 6 hole) and a shortstop’s bearing of -15 would result in a delta bearing of 5 (-15 – -20 = 5). Because the left side of the diamond has negative values and the right side has positive values, we can differentiate between a defender needing to range glove side or arm side. This distinction is important as instinctively we know that 5 degrees left is easier than ranging 5 degrees to the right. Fortunately, the data bears this out. For BBE to the shortstop with an exit velocity between 83 and 90 mph and a delta bearing between 8 and 12 (requiring the ss to range in the hole between short and third), the out probability is 16.5% while the out probability of -8 and -12 (requiring the ss to range up the middle) is 62.4%.
OAA Data Table
The OAA metrics table separates an OAA into several component parts to allow the user to go deeper and better understand how a defender earned his OAA score. A defender’s score is broken down by position and year to allow a quick view of how defensive ability has changed. In addition, we divide all his opportunities into 4 bins based on the out probability: routine, 50/50, difficult, and impossible (impossible is anything with an out probability less than 1 percent). This view allows a user to see how a player does on each type of opportunity.
For example, Alex Lodise, Florida State’s shortstop and Golden Spikes Award finalist, earned an incredible 12.12 OAA score. The table breaks down his 188 chances into the following bins: 81 routine, 54 50/50, and 53 difficult plays (impossible plays don’t contribute to total chances). We calculate the expected out probability for each of Lodise’s 3 bins. For the routine bin, it is 84%. We then compare this mean to Lodise’s actual conversion rate on these BBEs (90%). The difference between the two is the net (6%). These same calculations are repeated for each bin and results are in the net column.
OAA Data Table Example – FSU’s Alex Lodise
The table also includes an OAA per 100 metric. Because OAA is a cumulative stat, the number of opportunities very much influences the overall OAA. OAA per 100 seeks to level the playing field with respect to the number of chances. To see how this works, we’ll compare Kolby Branch from the University of Georgia to Steven Milam from LSU. In 2025, Steven Milam was an elite shortstop, third overall by OAA totaling 16.02. Kolby Branch was a great shortstop earning 12.19 OAA. Milam earned his score from 214 opportunities, while Branch had just 93. OAA per 100 calculates what each player would have earned if each had the same number of opportunities. By this metric, Branch has 13.1 OAA per 100 while Milam has a still very good 7.5 OAA per 100.
Player Percentile Rankings
OAA Player Percentile Example – FSU’s Alex Lodise
Our player percentile ranking sliders help further contextualize the data. From the table above, we see that Lodise had an OAA per 100 of 6.44. While the positive sign indicates he was a better than average defender, we may not know how much better he was relative to other defenders. The sliders tell you his 6.44 OAA per 100 ranks in the 85th percentile of Division 1 infielders.
Customization
As with all our features, OAA allows the user to get exactly the view they would like using our intuitive filters. Whether it is time, difficulty, result, position or any combination thereof, you can get the view that is most relevant and consumable for your specific use case.
Outs Above Average Filtering Options
Conclusion
OAA represents a significant step forward in the ability to quantify and measure defensive ability. We can now assign, with statistical rigor, an out probability of any BBE to any position given we know how far a defender must range to make a play and how long they have to do it. With a large enough sample, we can use this information to evaluate how much better or worse defenders are than an average one. However, there’s still more to do. As more data becomes available, we plan to update to consider additional factors like a runner’s speed for ground balls. Future consideration will also be given to understanding how the outfield fence influences catch probability. At 6-4-3 Charts, we pride ourselves on customer service and responsiveness. We look forward to feedback on this metric and working with coaches and analysts on how it can best support their program.