Minors to the Majors: Home-to-First Time Analysis

Finally. I’ve always wondered why home-to-first times weren’t publicly available. It seems like every other stat is tracked but not the information every scout is tracking. Now I’ve got my hands on the data to analyze. It’s still not publicly available yet but after quite a bit of nagging, I was able to convince MLBAM to let me have the 2016 season data. Here’s my initial breakdown.

I needed to get the data in a useable format with an idea of the hitter’s top speed. Every hitter doesn’t go all out to first base on every play. I wanted just the top times. The problem with just using the best times was many were bunts. Historically, home-to-first times are calculated from contact on a normal swing to when the batter touches first base. Here is an example with Mike Trout.

With bunts, players are already heading out of the box like Adam Eaton is doing here.

Also, if there was an error involved with the bunt, the play got labeled as an error, not a bunt. For this reason, I removed all plays with errors. I know I will miss some possible times but super the low ‘bunt-error’ times were throwing off the results.

At this point, I needed to decide which times to use. I went with an average of the player’s top 5 values with a minimum 20 samples. I found the top times didn’t vary much after 20 samples.

For example, here are the top five runs for Nori Aoki. I’ve included them for two reasons. First, to show the accuracy of the times. Second, they are examples of jailbreaks. Normally these runs are faster since the batter is already heading to first base during the swing.

I don’t find it an issue with Aoki since he almost always does a weak running swing. It’s when other players do it, their times could get artificially lowered. I don’t know of a good way to correct the issue right now. One option would be to remove the one or two fastest times. I’m not sure this would work because I feel hitters either have a running swing or not at all. I’m really not sure how to deal with this issue.

Go ahead and break out your stopwatch and see how your times compare to those of Statcast. The time is from contact with the bat to the time the player first step on first base.

3.83 seconds

3.85 seconds

3.89 seconds

3.94 seconds

3.94 seconds

For me, the times were either dead on or a little slow. They aren’t completely off and give the data some value.

With the data sort of validated, I divided out the players by right and left-handed position player. Left-handed hitters start closer to first base and will need less time to get there. Here is the standard scale used across the scouting community to grade speed.

Industry Standard Home-To-First Times by Handedness
Grade RHH LHH
80 <4.0 <3.9
70 4.1 4.0
60 4.2 4.1
50 (Average) 4.3 4.2
40 4.4 4.3
30 4.5 4.4
20 4.6 4.5

The 50-grade runner is supposed to an average MLB runner. A runner with a 60-grade is supposed to be one standard deviation better than average. A 70-grade is two deviations better and so on. The same math applies to the lower grades.

So first, here are the average and median values for left and right-handed batters

Average Home-To-First Times by Handedness
LHH RHH Diff
Average 4.26 4.30 -0.04
Median 4.23 4.28 -0.04

The right-handed average is almost identical to the values used by scouts. The left-handed values are on average faster but closer to 0.05 than 0.1 seconds better.

Though the samples were reasonable (138 LH, 230 RH), we could be at a point in the game when the left-handed population is slower than their historical average. To verify the right-left split, I took the switch-hitters and found the difference in speed from the left and right side (min 20 times to first base from each side, 31 samples in all).

Difference in Home-To-First Times for Switch Hitters
Difference (LH-RH)
Average -0.09
Median -0.06

Neither value is up to 0.1 value. The average values average out to 0.65 seconds and the median values average to 0.50 seconds. While, the historic 0.1 seconds is not far off from my values, a nice round 0.5 is probably more correct.

Next, I found the standard distribution of values for the right and left-handed hitters. With the standard deviation, I was able to calculate the times for each grade. For reference, I included the overall best and worst times from each group.

Distribution of Home-To-First Times (2016)
Value LH RH
Median 4.23 4.28
Standard Deviation 0.20 0.20
Min 3.82 3.91
80 3.66 3.71
70 3.86 3.91
60 4.06 4.10
50 (average) 4.26 4.30
40 4.45 4.49
30 4.65 4.69
20 4.85 4.89
Max 4.87 4.84

It is pretty crazy to see both the standard deviations work out to 0.20 seconds. This point is HUGE and is the MAIN point to take away from the article. The increase in deviation shows that the standard speed scale has too many players grouped into the really fast and really slow. When dealing with one of these players, find out how close the player is the historic cutoffs and should the player be graded at 90 or 10-Grade speed.

I don’t see the old grading system changing. The key is to know where the biases exist and adjust expectation accordingly.

With the background information out of the way, here is a list of the league’s fastest and slowest players (full list).

Best and Worst Home-To-First Times (2016)
Name Home-to-First Bats
Billy Burns 3.77 B
Billy Hamilton 3.82 B
Dee Gordon 3.82 L
Kevin Kiermaier 3.87 L
Norichika Aoki 3.89 L
Starling Marte 3.91 R
Jose Iglesias 3.91 R
Rajai Davis 3.91 R
Adam Eaton 3.92 L
Odubel Herrera 3.92 L
Tommy Joseph 4.75 R
Ryan Howard 4.76 L
Rene Rivera 4.78 R
Justin Smoak 4.78 B
Yasmani Grandal 4.79 B
Curt Casali 4.79 R
Roberto Perez 4.80 R
Albert Pujols 4.84 R
Dioner Navarro 4.85 B
Brian McCann 4.87 L

The list is not surprising with the league’s top burners at the top and the slow first basemen and catchers at the bottom.

For now, that is it for manipulating the home-to-first times. I’m still wrapping my head around the information so more analysis will be coming. Also, let me know if you have any question or need any information cleared up. I am sure I haven’t thought of everything. Until then, happy prospecting.



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Jeff writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won three FSWA Awards including on for his MASH series. In his first two seasons in Tout Wars, he's won the H2H league and mixed auction league. Follow him on Twitter @jeffwzimmerman.

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Anon
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Anon

Because you know you are going to look it up – Mike Trout is 50th at 4.05.

Total of 427 players on the list.

Fastest catcher – Chris Herrmann at 61 (4.07). Willson Contreras not too far behind at 78 (4.09)