2022 Season Recap

Weighted Errors Season Recap 2022

The game of baseball has experienced notable progress in recent years in the way that players have been evaluated with advanced statistics. Though offensive statistics have grown by leaps and bounds, it seems that analysis of players based on their defensive performance has remained effectively the same. While there have been notable discoveries with stats such as Outs Above Average (OAA) and Defensive Runs Saved (DRS), there is an untapped opportunity to analyze a player’s errors to adjust their overall defensive effectiveness. Errors remain one of the most subjective stats in baseball. While seemingly every game event can be broken down into thousands of different parts, errors are still somewhat basic. Currently, there are websites that break down errors into throwing and fielding errors, showing which part of the play that the error occurred. While this is beneficial, it still does not tell the whole story. I have researched a way that puts a value on errors based on the outcome of the error as well as the situation of the game. This analysis might be considered similar to breaking down a quarterback’s interceptions in football. Consider a scenario where two quarterbacks throw an interception in a given game. One interception is on a 50-yard “Hail Mary” as time expires in the first half, while the other occurs on first and ten in the opponent’s red zone. Though each quarterback would be assessed the same statistic of one interception, clearly the “Hail Mary” play is not nearly as detrimental to the team’s success as the latter. All interceptions are not equal. Similarly, all errors are not equal (just about all plays in a given game could be described similarly).

The main premise of the idea is that some errors made by fielders are more costly  than others. This is not an outlandish idea, in fact it is one that has been replicated in other aspects of the game, especially hitting. Different hits are valued more than others as seen in “Slugging Percentage”, valuing extra base hits more than singles. So why not do this with errors?  I have attempted to create a weighted value for fielding errors by analyzing  the bases given up and multiplying it by the situation in which it occurred. My method takes into account two major factors. First, I have calculated the bases that are given up on each error by looking at archived video and play by play descriptions. This has allowed me to calculate the number of bases that the offensive team gained on an error by the fielding team. While it is somewhat cumbersome doing this in retrospect, there should be an easy way to do it as a part of an official scoring decision, or by a statistician who is entering gameday data for a particular team. Additionally, I have used FanGraphs’ “leverage index”, which calculates the situation in a game by a plate appearance-by plate appearance basis. This number has served as my baseline for how detrimental an error is in a given situation

Here is an easy view into two different situations that should be treated much differently than they are currently. 

In the videos below we see examples of errors impacting a game in different ways. In the first video, we have a blowout at Fenway Park. Alex Bregman is fielding a ground ball in the bottom of the 8th inning with a nine run lead. He misplays it and batter Jackie Bradley Jr. reaches on the error. Bradley’s presence on first base really has little to no effect on the outcome of the game that night. In the spreadsheet, we can see that the leverage index for that at bat was 0.01, effectively 0. 

The second video had the biggest “error penalty” for non-interference plays that I looked at over the course of the 2022 season. The bases are loaded in the bottom of the 8th inning at Progressive Field in Cleveland. Carlos Correa makes a great diving stop in the hole to keep the ball on the infield but throws wildly to third base. Two runs scored on the play and the Guardians tied the game on that swing. The leverage index number on that situation was 6.05! 

(see videos below)

There are still discretionary ways that I have evaluated some plays over others, and that is where some criticism comes into play. On this play, I did not penalize Correa on the runner scoring from third base because he was (likely) going to score on the play regardless. As this process becomes commonplace for evaluation, my hope is that there will be a better way to catalog the bases that are gained on each play. But, using my model, we will see that there is another important layer beyond conventional fielding percentage.  Now we must tie in FanGraphs’s “Leverage Index”. Again, this is the value placed on individual situations. I have looked at each error's relation to the leverage index of the play to calculate the overall penalty for each error. 

To read more about Leverage Index, read here: https://library.fangraphs.com/misc/li/

As stated earlier, errors by themselves are incredibly subjective and some players who are able to reach more balls may have a disproportionate number of errors compared to his less “rangey” or athletic counterpart. To combat this, I have introduced adding elements of range and positioning into my calculation. For this, I have consulted Statcast’s Outs Above Average (OAA) and Field Bible’s Defensive Runs Saved (DRS). While these metrics have their flaws in evaluating different positions, they were the most beneficial for factoring in terms of range and ability. I have incorporated these statistics in an attempt to create an all encompassing defensive value for these players, including the weighted errors idea.

You can find the links here:

https://baseballsavant.mlb.com/leaderboard/outs_above_average

https://fieldingbible.com/DRSLeaderboard

https://baseballsavant.mlb.com/catcher_framing?year=2022&team=&min=q&type=catcher&sort=4,1

By using the three factors: Weighted Errors, Defensive Runs Saved, and Outs Above Average, (Note: Catchers are graded on their “Catching Runs Saved” on BaseballSavant so that is their other variable in the ranking) I have used the existing rankings for all qualified fielders to create a total defensive score. My equation has placed more value on DRS and OAA dependent on infield/outfield position over Weighted Errors because it is more indicative of performance, though the errors do play a factor. With all three factors shown in the research, the hope is that evaluating players becomes more indicative to their overall performance, including the plays that they make and the ones they do not.

The advantage of viewing the rankings side by side is to show how certain statistics value certain players. Obviously, some players shine greater in some categories over others, though a single “score” or rating is beneficial to see. For example, Jose Trevino leads all qualified catchers in OAA and DRS, though ranks 8th in fielding percentage. After dissecting his errors, I found that he had low base giving and low leverage errors. After running the calculation, we see that Trevino benefits from his high ranking in other statistics and comes out as the top rated catcher in the MLB. The same can be seen of 2B Andres Gimenez of the Guardians. My research shows that he did, in fact, make some costly errors as evidenced by his 29.31 penalty on errors (3rd most among secondbasemen). His other metrics are quite good however, and compensate for his errors. After running the calculation, he comes out as the #2 2B in the MLB behind Jonathan Schoop. The opposite effect of the ranking also stands out in the calculations, especially for impending free agent shortstop Carlos Correa. Correa originally enjoyed the fourth overall ranking on fielding percentage for shortstop, though the combination of his errors as well as the other metrics have knocked him down to 18th among 22 qualified shortstops for the 2022 season.

Trevino:

.9898 ADJFPCT (Rank: 6), 15 CRS (Rank: 1), 21 DRS (Rank: 1) / Overall Score =.708 (Rank: 1)

Gimenez:

.9620 ADJFPCT (Rank: 10), 11 OAA (Rank: 2), 16 DRS (Rank: 2) / Overall Score = 1.267 (Rank: 2)

Correa:

0.9601 ADJFPCT (Rank: 12), -3 OAA (Rank: 19), 3 DRS (Rank: 12) / Overall Score = 4.875 (Rank: 18)

Of course, statistics do not, by themselves, tell the entire story of how a game or season transpires. However, as statistics play a greater and greater role in how the game is played (shifts, pitches offered to particular batters in particular situations, etc.), a deeper understanding of defensive metrics can help players and teams evaluate themselves and adjust accordingly. Weighing errors is a viable option to add more nuance to a statistic that desperately needs to be adjusted. The play itself does not tell enough of the story for errors, we need more context to truly grade the play. With our new understanding, errors become more complex, though we gain a more logical process in evaluating these plays. Noting an error as “1” in a scorebook does not do the play justice. As we continue our evolution of  diving deeper into statistics, weighting errors and incorporating these plays into existing metrics will yield a more meaningful analysis as to how valuable a fielder was to his team. I am by no means trying to show that I have solved a complex and dated statistic, but rather showing the nuance that is created in some of these plays. It is important to remember context in these plays and I hope to be able to look deeper into defensive metrics in regards to player movement, ball speed, and alignment in the future. 

Thank you for reading and visiting the site. The workbook is attached below as well for your viewing pleasure. 

 I would love to hear your thoughts/comments/questions/suggestions at 

duffydigest@gmail.com !

Drew Duffy 

October 16, 2022