The A's were 5-2 at the end of Week #3 of the 2017 season and currently sit in 2nd place in the AL West.
It was a very successful week for the A's. They won both series at home against the Rangers and Mariners and currently have a 10-9 record overall.
In this article, I am going to do something different. As I mentioned in last weeks article, I created an original stat that gives fans another way to evaluate batted balls. My stat, BAHP, is the Expected Batting Average in terms of Hit Probability Percentage. I gather all the data from the Gamefeed tab under Applications on Baseball Savant.
In these charts below, I am presenting the number of Batted Ball Events (BBE) with a HP% of 75% and above, BBE with a HP% between 26%-74%, the Total Number of Expected hits, the Total Number of BBE, and the BAHP for every batted ball event this year by A's hitters. The last chart is a Ranking of A's hitters in terms of highest BAHP. BAHP is calculated as follows: If a HP% is greater than or equal to 75%, regardless of the outcome, then consider the batted ball event a hit (1.0). If a HP% is below 25%, regardless of the outcome, consider the batted ball event an out. Any batted ball events between 26% and 74% are going to be hits 50% of the time, so reward the hitter with 1/2 of a hit or (0.5). Simply add up all the (1.0) and (0.5) hits then divide this number by the total number of batted ball events to calculate BAHP.
BAHP is unique because it sees beyond the final result of the batted ball event.
For example, in Game #14 when the A's played the Rangers at home, Adam Rosales hit a ball off Yu Darvish with an Exit Velocity of 97.9 MPH, at a 35 degree Launch Angle which traveled 367 feet. This event had a Hit Probability of 27%, but actually cleared the wall for a Home run. HP% says that if a player were to hit this ball with the same dimensions as Rosey did, the event would be a hit only 27% of the time. Given that this batted ball had only a 27% probability of being a hit, BAHP just barely marks this event as half a hit.
Conversely, in Game #2 when the A's played the Angels at home, Matt Joyce hit a ball off Matt Shoemaker with an EV of 74.4 MPH, at a 18 degree Launch Angle, which traveled 166 feet. This event had a Hit Probability of a whopping 92%, but actually resulted in a Lineout. Given that this batted ball had a 92% probability, meaning 92 out of 100 times this ball is going to land for a hit, this event gets marked as a (1.0) hit in terms of BAHP because it exceeded my 75% HP threshold.
So what does BAHP actually tell us? Well, from an statistical sense, a player with a high BAHP has been hitting the ball hard, at an optimal launch angle, and with high probability of being a hit. From an intuitive sense, BAHP is a more accurate way of determining how well a player is actually hitting the ball as opposed something like batting average. Often times baseball fans get a sense that a certain hitter is "due" to get some hits. Maybe it's because he's taking pitches well, or more than likely, he's been hitting the ball hard, but he just hasn't found any holes in the defense. BAHP is a predictive stat that will tell you which hitters have been making solid contact, but may not be lighting up the box scores in terms of batting average, RBI's, OBP, etc. I am still working on a "rule of thumb" for BAHP, like Weighted On Base Average (wOBA)'s rules of thumb. In order to determine this scale, I will look at all batted ball events from the 2017 season thus far and will calculate how to scale BAHP. I've attached a link to my spreadsheet which contains all batted ball events with a calculated HP% by Baseball Savant. I update this spreadsheet daily and would like to figure a way how to gather game data automatically instead of having to manually input the data.
As far as the A's go, they have an off day tomorrow and will head down to Anaheim for a 3 game set with the Halos, then another 3 game set with the 1st place Houston Astros. Projected Starting Pitching match ups are as follows:
Game #20 (4/25) Probable Starters: Jesse Hahn vs. JC Ramirez
Game #21 (4/26) Probable Starters: Sean Manaea vs. Ricky Nolasco
Game #22 (4/27) Probable Starters: Kendall Graveman** vs. Matt Shoemaker
Game #23 (4/28) Probable Starters: Jharel Cotton vs. Charlie Morton
Game #24 (4/29) Probable Starters: Andrew Triggs vs. Joe Musgrove
Game #25 (4/30) Probable Starters: Jesse Hahn vs. Dallas Keuchel
**: Graveman is projected to make his return from the DL against the Angels.
Weeks 1-3 Oakland A's Hitter BAHP Calculations:
It was a very successful week for the A's. They won both series at home against the Rangers and Mariners and currently have a 10-9 record overall.
In this article, I am going to do something different. As I mentioned in last weeks article, I created an original stat that gives fans another way to evaluate batted balls. My stat, BAHP, is the Expected Batting Average in terms of Hit Probability Percentage. I gather all the data from the Gamefeed tab under Applications on Baseball Savant.
In these charts below, I am presenting the number of Batted Ball Events (BBE) with a HP% of 75% and above, BBE with a HP% between 26%-74%, the Total Number of Expected hits, the Total Number of BBE, and the BAHP for every batted ball event this year by A's hitters. The last chart is a Ranking of A's hitters in terms of highest BAHP. BAHP is calculated as follows: If a HP% is greater than or equal to 75%, regardless of the outcome, then consider the batted ball event a hit (1.0). If a HP% is below 25%, regardless of the outcome, consider the batted ball event an out. Any batted ball events between 26% and 74% are going to be hits 50% of the time, so reward the hitter with 1/2 of a hit or (0.5). Simply add up all the (1.0) and (0.5) hits then divide this number by the total number of batted ball events to calculate BAHP.
BAHP is unique because it sees beyond the final result of the batted ball event.
For example, in Game #14 when the A's played the Rangers at home, Adam Rosales hit a ball off Yu Darvish with an Exit Velocity of 97.9 MPH, at a 35 degree Launch Angle which traveled 367 feet. This event had a Hit Probability of 27%, but actually cleared the wall for a Home run. HP% says that if a player were to hit this ball with the same dimensions as Rosey did, the event would be a hit only 27% of the time. Given that this batted ball had only a 27% probability of being a hit, BAHP just barely marks this event as half a hit.
Conversely, in Game #2 when the A's played the Angels at home, Matt Joyce hit a ball off Matt Shoemaker with an EV of 74.4 MPH, at a 18 degree Launch Angle, which traveled 166 feet. This event had a Hit Probability of a whopping 92%, but actually resulted in a Lineout. Given that this batted ball had a 92% probability, meaning 92 out of 100 times this ball is going to land for a hit, this event gets marked as a (1.0) hit in terms of BAHP because it exceeded my 75% HP threshold.
So what does BAHP actually tell us? Well, from an statistical sense, a player with a high BAHP has been hitting the ball hard, at an optimal launch angle, and with high probability of being a hit. From an intuitive sense, BAHP is a more accurate way of determining how well a player is actually hitting the ball as opposed something like batting average. Often times baseball fans get a sense that a certain hitter is "due" to get some hits. Maybe it's because he's taking pitches well, or more than likely, he's been hitting the ball hard, but he just hasn't found any holes in the defense. BAHP is a predictive stat that will tell you which hitters have been making solid contact, but may not be lighting up the box scores in terms of batting average, RBI's, OBP, etc. I am still working on a "rule of thumb" for BAHP, like Weighted On Base Average (wOBA)'s rules of thumb. In order to determine this scale, I will look at all batted ball events from the 2017 season thus far and will calculate how to scale BAHP. I've attached a link to my spreadsheet which contains all batted ball events with a calculated HP% by Baseball Savant. I update this spreadsheet daily and would like to figure a way how to gather game data automatically instead of having to manually input the data.
As far as the A's go, they have an off day tomorrow and will head down to Anaheim for a 3 game set with the Halos, then another 3 game set with the 1st place Houston Astros. Projected Starting Pitching match ups are as follows:
Game #20 (4/25) Probable Starters: Jesse Hahn vs. JC Ramirez
Game #21 (4/26) Probable Starters: Sean Manaea vs. Ricky Nolasco
Game #22 (4/27) Probable Starters: Kendall Graveman** vs. Matt Shoemaker
Game #23 (4/28) Probable Starters: Jharel Cotton vs. Charlie Morton
Game #24 (4/29) Probable Starters: Andrew Triggs vs. Joe Musgrove
Game #25 (4/30) Probable Starters: Jesse Hahn vs. Dallas Keuchel
**: Graveman is projected to make his return from the DL against the Angels.
Weeks 1-3 Oakland A's Hitter BAHP Calculations:
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Adam Rosales | 1 | 1 | 1.5 | 3 | 0.500 |
2 | Adam Rosales | 1 | 2 | 2 | 6 | 0.333 |
3 | Adam Rosales | 1 | 8 | 5 | 16 | 0.313 |
Totals | Adam Rosales | 3 | 11 | 8.5 | 25 | 0.340 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
3 | Bruce Maxwell | 2 | 0 | 2 | 4 | 0.500 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
3 | Chad Pinder | 2 | 1 | 2.5 | 6 | 0.417 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
2 | Jaff Decker | 3 | 0 | 3 | 4 | 0.750 |
3 | Jaff Decker | 2 | 3 | 3.5 | 9 | 0.389 |
Totals | Jaff Decker | 5 | 3 | 6.5 | 13 | 0.500 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Jed Lowrie | 2 | 9 | 6.5 | 19 | 0.342 |
2 | Jed Lowrie | 4 | 6 | 7 | 15 | 0.467 |
3 | Jed Lowrie | 1 | 6 | 4 | 15 | 0.267 |
Totals | Jed Lowrie | 7 | 21 | 17.5 | 49 | 0.357 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Josh Phegley | 0 | 2 | 1 | 5 | 0.200 |
2 | Josh Phegley | 0 | 1 | 0.5 | 2 | 0.250 |
3 | Josh Phegley | 1 | 7 | 4.5 | 10 | 0.450 |
Totals | Josh Phegley | 1 | 10 | 6 | 13 | 0.353 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Khris Davis | 5 | 7 | 8.5 | 15 | 0.567 |
2 | Khris Davis | 4 | 7 | 7.5 | 15 | 0.500 |
3 | Khris Davis | 2 | 2 | 3 | 9 | 0.333 |
Totals | Khris Davis | 11 | 16 | 19 | 39 | 0.487 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Marcus Semien | 2 | 5 | 4.5 | 11 | 0.409 |
2 | Marcus Semien | 1 | 3 | 2.5 | 8 | 0.313 |
Totals | Marcus Semien | 3 | 8 | 7 | 19 | 0.368 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Mark Canha | 0 | 3 | 0.5 | 7 | 0.214 |
2 | Mark Canha | 0 | 2 | 1 | 4 | 0.250 |
Totals | Mark Canha | 0 | 5 | 2.5 | 11 | 0.227 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Matt Joyce | 2 | 5 | 4.5 | 14 | 0.321 |
2 | Matt Joyce | 3 | 4 | 5 | 11 | 0.455 |
3 | Matt Joyce | 2 | 3 | 3.5 | 12 | 0.292 |
Totals | Matt Joyce | 7 | 12 | 13 | 37 | 0.351 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Rajai Davis | 0 | 8 | 4 | 17 | 0.235 |
2 | Rajai Davis | 2 | 3 | 3.5 | 8 | 0.438 |
3 | Rajai Davis | 0 | 4 | 2 | 9 | 0.222 |
Totals | Rajai Davis | 2 | 15 | 9.5 | 34 | 0.279 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Ryon Healy | 6 | 4 | 8 | 19 | 0.421 |
2 | Ryon Healy | 2 | 2 | 3 | 10 | 0.300 |
3 | Ryon Healy | 2 | 7 | 5.5 | 16 | 0.344 |
Totals | Ryon Healy | 10 | 13 | 16.5 | 45 | 0.367 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Stephen Vogt | 4 | 5 | 6.5 | 16 | 0.406 |
2 | Stephen Vogt | 2 | 2 | 4 | 10 | 0.400 |
3 | Stephen Vogt | 1 | 5 | 3.5 | 11 | 0.318 |
Totals | Stephen Vogt | 8 | 12 | 14 | 37 | 0.378 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Trevor Plouffe | 2 | 8 | 6 | 13 | 0.462 |
2 | Trevor Plouffe | 3 | 4 | 5 | 11 | 0.455 |
3 | Trevor Plouffe | 3 | 3 | 4.5 | 13 | 0.346 |
Totals | Trevor Plouffe | 8 | 15 | 15.5 | 37 | 0.419 |
Week | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Yonder Alonso | 1 | 8 | 5 | 15 | 0.333 |
2 | Yonder Alonso | 1 | 2 | 2 | 7 | 0.286 |
3 | Yonder Alonso | 3 | 5 | 5.5 | 12 | 0.458 |
Totals | Yonder Alonso | 5 | 15 | 12.5 | 34 | 0.368 |
Rank | Player | 75%+ (1.0) | 26%-74% (0.5) | TotHits | TotBBE | BAHP |
---|---|---|---|---|---|---|
1 | Bruce Maxwell | 2 | 0 | 2 | 4 | 0.500 |
2 | Jaff Decker | 5 | 3 | 6.5 | 13 | 0.500 |
3 | Khris Davis | 11 | 16 | 19 | 39 | 0.487 |
4 | Trevor Plouffe | 8 | 15 | 15.5 | 37 | 0.419 |
5 | Chad Pinder | 2 | 1 | 2.5 | 6 | 0.417 |
6 | Stephen Vogt | 8 | 12 | 14 | 37 | 0.378 |
7 | Yonder Alonso | 5 | 15 | 12.5 | 34 | 0.368 |
8 | Marcus Semien | 3 | 8 | 7 | 19 | 0.368 |
9 | Ryon Healy | 10 | 13 | 16.5 | 45 | 0.367 |
10 | Jed Lowrie | 7 | 21 | 17.5 | 49 | 0.357 |
11 | Josh Phegley | 1 | 10 | 6 | 13 | 0.353 |
12 | Matt Joyce | 7 | 12 | 13 | 37 | 0.351 |
13 | Adam Rosales | 3 | 11 | 8.5 | 25 | 0.340 |
14 | Rajai Davis | 2 | 15 | 9.5 | 34 | 0.279 |
15 | Mark Canha | 0 | 5 | 2.5 | 11 | 0.227 |
baseballsavantdata.xlsx |