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Feb 7, 2014 · But let’s take a look at how batted ball types correlate with a bonus stat I added into the correlation tool: Hits/Batted Ball, (let’s call it H/BatBall for short) which are hits divided...
Feb 3, 2011 · Sitting and watching or scoring a game, any casual fan can deduce that a batter who gets 1 hit in 4 at-bats in a game is batting .250 for the game. However, the average fan attending a game can’t do the equivalent with advanced fielding statistics.
Feb 17, 2010 · Batted ball statistics, like most statistics, should be used with caution for three key reasons. First, sample size is very important for the batted ball stat you likely care most...
- Introduction
- Basic Process
- The New ‘Barrels’ Statistic
- Mixing and matching: Statcast and Sabermetrics
- Barrels: Relationships with Other Statistics
- Locating Luck
- Finding ESLG
- Finding Eiso
- Finding Ehr/G
- Using Eslg, Eiso and Ehr/G
Statcast—MLB’s player-tracking, ball-tracking, everything-tracking tool—has improved in accuracy and volume each year since its inception. The data it provides are uniquely valuable. Thus, we need to ask an important question: How can we put these data to good use? My purpose in writing this article is to create a set of statistics that measures ho...
I examined Statcast results for batters with at least 150 batted ball events (balls put in play). I combined sabermetrics and Statcast data in a spreadsheet of 407 hitters from the 2015 and 2016 seasons, then mixed and matched different variables to evaluate positive or negative correlations. I wanted to see which Statcast variables correlated high...
MLB’s newest Statcast treasure is called Barrels. It measures a player’s ability to put the barrel of the bat on the ball and generate good contact. Per MLB.com, “A barrel is defined as a well-struck ball where the combination of exit velocity and launch angle generally leads to a minimum .500 batting average and 1.500 slugging percentage.” The “ba...
As some preliminary research, I ran linear regression analyses on Statcast and advanced analytics variables, as displayed in Table 1 below. Their R-squared values—which show correlation, with a higher value meaning the two variables are more closely associated—are listed.
The first thing we can note is that Barrels Per Plate Appearance, known henceforth as B/PA, has high correlations with three statistics: Isolated Power (ISO), Home Runs Per Game (HR/G) and Slugging Percentage (SLG). Graph 1 shows the B/PA-SLG relationship. Graph 2 shows the B/PA-ISO relationship. Graph 3 shows the B/PA-HR/G relationship. Slugging P...
I wanted to see which players in 2015 got “unlucky,” meaning they hit a high percentage of balls on the barrel of the bat and at a good launch angle, but weren’t rewarded with high slugging percentages, high isolated power numbers, or an appropriate amount of home runs. In the next sections, I’ll run through how we can establish who was “lucky” and...
To find expected slugging percentage (eSLG) based on B/PA, I first ran the linear regression analysis, then used R numerical summaries to determine the equation of the least squares regression line. The equation was y = 2.0553X + 0.349. Plugging in B/PA as the x-variable, I found eSLG for each qualifying player. Finally, I subtracted eSLG from aSLG...
Determining Expected Isolated Power (eISO) for a player is similar to how we found eSLG. The equation for eISO was y = 1.982412X + 0.083254. Simply plug in the player’s B/PA percentage and the result will be what his ISO should havebeen based on how often he hit the ball on the sweet spot of the bat. Here are the “unluckiest” players of 2015, based...
The final statistic we’ll develop is Expected Home Runs Per Game, or eHR/G. Once again, we’re focusing on home runs as one of the three main stats because it holds such a strong correlation with Barrels. The process is pretty much the same as it was for finding eSLG and eISO, so I won’t go into great detail. The equation for eHR/G was y = 339.348X ...
How can we use the three expected statistics? They shouldn’t be the most decisive factor when a ball club makes choices regarding acquiring players or letting them go. But the concept is similar to Pythagorean Wins, which tell us how many wins a team should havegiven itsrun differentials. For example, the Texas Rangers have the best record in the A...
Statistics such as field independent pitching (FIP), batting average on balls in play (BABIP), weighted on-base average (wOBA) and wins above replacement (WAR) have entered the public sphere and are commonly cited as ways to assess a
In total, there are 121 statistics in baseball. Of those 121 statistics, 72 baseball statistics are considered “standard” while 49 baseball statistics are considered “advanced”. In addition to the standard and advanced stats, there are another 32 statistics that the MLB labels as “Statcast”.
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Mar 28, 2019 · Analytics, also called sabermetrics, rule baseball front offices, and on-field decision making. To most fans, they’re just a confusing or misunderstood topic. Some people think they ruin...
