What defensive metrics have the **wholesale baseball jersers** most impact **philadelphia phillies jersers** on a team's Win-Loss record?

Basic defensive metrics like errors, assists and putouts are readily available metrics, **pittsburgh pirates jersers** but to tell how the standings are affected, a run value needs to be placed on the play that the player did or not make. To get this data an in-house metric would need to be created. The extent of this type of data available is limited, **atlanta braves jersers** but with enough time, it can be created. The data needs to be collected on a group of teams (division to entire league) to be able to compare results. If the data was just collected for the team, it can show that they made 10 great diving catches to stop some hits, but how does that compare to other teams.

To get the best information for this in-house defensive metric, three main pieces of information **chicago white sox jersers** are needed.

The first data needed is how hard and in what direction the ball was hit. Hit F/X data, supplied to **blue avisail garcia authentic jersers** all teams, gives the direction and speed that each ball was hit. If this data is not available to the team, BIS and STATS provide data on where each ball was hit on the field in various zones.

The next set of data is not currently available is the location of the defenders. This data could be obtained by watching every play of every game (or some subset of teams). Also, you could take the hitter tendencies (pull hitter, hits to all fields, etc) and find the average position of the fielder. Getting the position of each player may be rather difficult and time consuming. If the resources are st. louis cardinals jersers available I would collect all the data on each ball in play. If not, I would collect a sample of data and see if it matters significantly where the defender is positioned. If we **chicago white sox jersers** know that the amount of variation depending of defensive positioning to be 4 plays per 100 hit in the player's direction, **los angeles angels of anaheim jersers** **los angeles angels of anaheim jersers** this level of variation can be added in later.

Finally data would need to be obtained if the player made the catch, threw it to for an out or caused and error. This data is readily available from STATS or BIS or could be obtained easily from the mlb.com which as all the data in easy to get .xml format.

Once the data has **cincinnati reds jersers** been collected, a run expectancy chart needs **tampa bay rays jersers** to be generated for the league. It shows the average number of runs generated given the runners on base and the number of outs in an inning. Say a batter hits the ball and gets to first base on a ball barely missed by the shortstop. The average number of runs with a runner on first with no outs could be 1.1 runs (example). Now if the fielder got to the ball and threw out the hitter, it would be 1 outs with no one on base. The average number of runs scored in this situation would could be 0.4 runs. So by making or not making the play, the average number of runs expected to be scored changes by 0.7. This is the run value for the play.

The data between different players can then be compared to see which players made the most plays given the situation to determines the average percentage of times any given player makes a play. Once this percentage is known, then runs lost or saved can begin to be assigned to players. For example, 50% of the time a shortstop gets the runner out at 1st for a ball hit 4 steps to his left. A shortstop, only makes 3 out of 10 of these plays, cost the team on average 1.4 runs.

Finally, the number of runs saved or lost per team would be known and these values would be summed. Over the past few years, 10 runs prevent or scored is needed to get an additional win. Taking the difference in runs prevent above or below average and dividing it by 10 will give the number of wins or losses that a teams defense generates.

9 months ago
2018-04-17T07:49:01Z