Statcast FIP: Estimate The Home Runs

There are numerous ways to use Statcast data to estimate FIP, many involving various methods for estimating HR/FB ratios using average fly ball distances or launch angles. I address the issue using a more granular approach, evaluating each batted ball on a case by case basis.  I am calculating the probability of the batted ball going over the fence by comparing it to all similarly hit balls dating back to the beginning of the 2015 season. Next, I sum all of these probabilities, and call it the estimated number of home runs. In theory, this number should be park and environment (temperature, altitude, weather) neutral, similar to xFIP in some ways.  I call this scFIP.

The following table shows the top 10 pitchers by scFIP. These pitchers have a minimum of 40 IP.

Top 10 Pitchers ranked by scFIP
est HR HR IP scFIP FIP
Noah Syndergaard 5.3 5 91.0 1.88 1.85
Clayton Kershaw 8.8 6 115.0 1.90 1.58
Jose Fernandez 5.2 5 87.7 2.07 2.04
David Phelps 1.3 2 40.0 2.13 2.37
Jake Arrieta 4.9 3 98.0 2.75 2.50
Stephen Strasburg 9.6 10 93.0 2.77 2.82
Steven Matz 5.4 5 72.3 2.78 2.71
Rich Hill 3.1 2 64.0 2.90 2.67
John Lackey 6.7 9 94.0 2.94 3.26
Lance McCullers 1.4 1 46.0 2.97 2.86
min 40 IP

This list certainly passes the eye test. Syndergaard, Kershaw, Fernandez, Arrieta, Strasburg. Mets fans should be happy to see two of their starting pitchers in the top 10.  These certainly are many of the top pitchers in MLB, although it is interesting to note the differences between this list and the top performers by the standard FIP:

Top 10 Pitchers ranked by FIP
est HR HR IP FIP scFIP
Clayton Kershaw 8.8 6 115.0 1.58 1.90
Noah Syndergaard 5.3 5 91.0 1.85 1.88
Jose Fernandez 5.2 5 87.7 2.04 2.07
David Phelps 1.3 2 40.0 2.37 2.13
Johnny Cueto 7.8 3 109.3 2.44 3.00
Jake Arrieta 4.9 3 98.0 2.50 2.75
Brad Brach 4.8 3 40.7 2.58 3.16
Rich Hill 3.1 2 64.0 2.67 2.90
Steven Matz 5.4 5 72.3 2.71 2.78
Chris Devenski 3.1 2 49.3 2.76 3.05
min 40 IP

Kershaw, Syndergaard, Fernandez and Phelps hog the top four spots in both lists, after which there is a bit of divergence.  FIP favors Cueto, Brach, and Devenski, where scFIP is more in line with Strasburg, Lackey, and McCullers. scFIP gives Kershaw nearly three additional estimated home runs over his season total of 6, which not only pulls him closer to the rest of the pack, but slides him into second place behind Noah Syndergaard. Similarly, scFIP gives Cueto nearly 5 additional estimated home runs, which explains why he failed to make the top 10, as he does with FIP. It does make some sense that these two particular pitchers would benefit from lower than expected home run totals, though, given their pitcher friendly home ballparks.

Pitchers with the 10 largest differences between scFIP and FIP by giving up more home runs than expected. (minimum 40 IP).

Top 10 Pitchers with more HR than estimated
est HR HR IP scFIP FIP dFIP
Ian Kennedy 11.6 18 81.7 4.44 5.46 -1.02
Chris Young 15.8 19 51.3 6.17 6.98 -0.81
Anibal Sanchez 10.3 14 66.3 5.00 5.73 -0.73
Juan Nicasio 8.8 12 63.3 4.21 4.87 -0.66
Alfredo Simon 11.4 14 53.3 6.56 7.19 -0.63
Hisashi Iwakuma 12.5 17 95.0 4.13 4.75 -0.62
Jorge De La Rosa 9.0 11 42.7 5.47 6.08 -0.61
Max Scherzer 13.6 18 101.3 3.13 3.70 -0.57
Archie Bradley 5.2 7 41.0 4.10 4.66 -0.56
Francisco Liriano 10.6 14 78.3 4.87 5.43 -0.56
min 40 IP

Right at the top of the list is Ian Kennedy, who has given up 6.4 more homers than you would expected based upon his batted balls alone. That is obviously a very large difference, and is close to equaling the difference between his season HR/9 (1.98) and career HR/9 (1.17). Subtract those 6.4 home runs from his total and his HR/9 would instead be 1.28. Of course, these home runs actually happened, so you cannot merely write them off, but it explains the difference between his ERA and somewhat bloated FIP. Going through the rest of his statcast based xstats, his value hit rate (my version of hard hit) has dropped significantly from 15% to 11%, his xOBA and xBABIP are both down, as is his average exit velocity, especially for line drives. Kennedy given up much weaker contact so far this season. It is unfortunate, then, that his home run figure is so much higher than you’d expect.

Max Scherzer also made his way onto this list, surrendering 4.4 more homers than you might expect from his batted balls. Scherzer has had a pretty strange season so far, it feels like he gets 10 strike outs and gives up a homers every game he pitches. Subtracting the 4.4 home runs from his total would push his HR/9 down to 1.21 from 1.60, still a very large increase from his career average of 1.02, and last season’s 1.06, but much, much better than what we have seen from him to this point. Scherzer has been struggling through bouts of weak command during his starts. Some may only last a few pitches, some longer. But, of course, it only takes one bad pitch for a major league hitter to put one over the wall, and Scherzer has certainly had more than his share of those so far this season. However, keeping in mind that a good many of these home runs may have been aided by either ballpark, environment, or both, and Scherzer’s home run struggles may not be as bad as they appear on first glance.

 

Pitchers with the 10 largest differences between scFIP and FIP by giving up fewer home runs than expected.  (minimum 40 IP).

Top 10 Pitchers with fewer HR than estimated
est HR HR IP scFIP FIP dFIP
Yordano Ventura 15.1 10 79.3 5.59 4.75 0.84
Jordan Zimmermann 12.9 8 86.0 4.38 3.63 0.75
Justin Nicolino 8.2 5 55.7 4.92 4.18 0.74
Chris Rusin 5.0 2 54.7 3.94 3.21 0.73
Sean Manaea 10.8 8 49.3 5.43 4.70 0.73
Tyler Duffey 14.3 11 59.7 5.40 4.68 0.72
Junior Guerra 10.3 7 61.3 4.55 3.86 0.69
Jake Peavy 11.4 8 72.3 4.54 3.92 0.62
Brad Brach 4.8 3 40.7 3.16 2.58 0.58
Adam Conley 9.7 6 83.3 4.32 3.75 0.57
min 40 IP

Switching over to pitchers who may be benefiting park and environmental effects to keep the ball in the yard, we see another Royals pitcher at the top of the list: Yordano Ventura. Given how poorly his season has gone, it may seem cruel to imagine it could actually get worse for him. By my estimate, he ought to have surrendered around 15 home runs to this point of the season, five more than the true total. Adding these extra home runs to his total would give him 1.71 HR/9, which is more than double his career average of .84 HR/9. This in addition to a drop in strike outs and a rise in walks, along with an increase in Value Hit rate (my version of hard hits). The exit velocity on his line drives is up over 3 mph, with an increase in launch angle as well, which explains the increase in home runs.

Jordan Zimmermann is an interesting case where, by FIP he has been solidly above average, but by scFIP (and also xFIP) he has actually been below average. He has 12.9 estimated home runs standing up to the 8 actual home runs he has given up during games. Assuming the 12.9 estimated home run total were true, it would amount of one of the highest home run rates of his career at 1.35 HR/9, far above his career average of .85. He had a worse rate in 2010, but he was coming back from Tommy John Surgery, and it was only a matter of 31 innings. This year, Zimmermann’s batted ball stats, overall, have been superior to his performance last season. He has given up more value hits, which accounts for the high estimated xSLG and estimated home run total, but his xOBA, xBABIP, and xAVG are all down. Overall, he has given up weaker and less valuable contact this season compared to last.

I’ve created a workbook with all of the scFIP and FIP scores this season up through today that you can look through at your leisure:

We hoped you liked reading Statcast FIP: Estimate The Home Runs by Andrew Perpetua!

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Andrew Perpetua is the creator of CitiFieldHR.com and xStats.org, and plays around with Statcast data for fun. Follow him on Twitter @AndrewPerpetua.

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Jim

I believe your team names don’t agree with the players listed.