In Game Velocity Changes – When Fatigue Attacks by Mike Sonne June 12, 2017 Velocity changes between seasons, or even games, are well reported on thanks to Jeff Zimmerman’s velocity tracking document. Fastball Velo changes, ’16 adjusted up C Anderson +2.4 Estrada +1.8 Cain -3.2 Santiago -2.8 Adleman -2.4https://t.co/Qr33MW0GYN — Jeff Zimmerman (@jeffwzimmerman) June 7, 2017 Velocity is very important to the success of pitchers – as I’ve written about with respect to my Stuff metric, and as highlighted in this great article by Mike Fast from 2010, every little bit of velocity matters. Within pitchers – those who lose velocity become shells of their former selves – like Eno Sarris wrote about Matt Harvey before this season started. While a Velocity drop between games is an indicator that someone might be hurt, a velocity drop within a game might indicate that a pitcher is becoming fatigue – a big sign of possible future injury. Gsellman’s velocity is falling off quickly in his starts, and I wrote about it today https://t.co/lclu5kFZNP — Gerald Schifman (@gschifman) May 1, 2017 I first want to start out by looking at in game velocity changes thoughout the entire season. I looked at all starts where a pitcher threw at least 5 innings. During each start, I looked at peak velocity in each inning, the average velocity in each inning, and the standard deviation of velocity in each inning. Only four seam, and two seam fastballs were included in this analysis – these had the highest overall velocity readings. I compared the peak and average velocities during the first inning, against those of the last inning in each start. Let’s look at this at a higher level first. I looked at the Velocity changes in these starts, by month Figure 1. Velocity change (between first, and final inning). A negative value represents a drop in velocity. If muscle fatigue truly is the demon myself and other researchers have made it out to be, this is a concerning finding. In the month of April, starters on average, lost nearly a full mile per hour between the first and the final inning in starts that lasted 5 innings or more. Where this analysis gets really interesting, is when you compare it against injury trends (these data provided by the Baseball Injury Consultants: http://baseballic.com/. Their analysis looks at the number of days lost for MLB players over the past three years, and the start of the season is far and away, the number one place for players to get hurt. A review of injuries by month performed by DeFroda at al., (2016), confirmed that most UCL injuries occurred in the first part of the season. So, as Bruce & Andrews (2015) stated, Muscle fatigue may contribute to a loss of stability in the elbow, which puts the UCL at risk. These findings indicate that pitchers may be more susceptible to fatigue in the early parts of the season, calling into question what kinds of training are done in the off season. Perhaps backing off of throwing completely isn’t the best way to keep arms healthy? So, how does a decrease, or increase in fatigue influence player performance? Garrett Chiado wrote one of my favourite pieces of the past few years, regarding the influence of the third time through the order on pitcher performance. He also included a nice, neat and tidy dataset on changes in performance metrics between the first, and the third time through the batting order. These data included average performance changes from the 2013 to 2015 season for starting pitchers. I compared the average, and maximum velocity changes for pitchers during these three seasons against the third time through the order data, to see if there was any effect of pitcher velocity increases/decreases on their performance through the order. It turns out – there is an interesting story to tell. Correlation (r) between change in average velocity/ maximum velocity, and performance metrics on the third time through the order Contact Rate Swstrk Rate Whiff Perc Fip K Rate Average -0.13 0.14 0.13 -0.19 0.11 Max -0.30 0.36 0.30 -0.13 0.47 Correlation (r2) between change in average velocity/ maximum velocity, and performance metrics on the third time through the order Contact Rate Swstrk Rate Whiff Perc Fip K Rate Average 2% 2% 2% 4% 1% Max 9% 13% 9% 2% 23% Looking at the correlation between average, and maximum velocity changes, and performance metrics, the significant relationships emerged. A decrease in velocity was significant associated with higher contact rates, lower swinging strike rates, a higher FIP, and lower strike out rate, when compared to the first time through an order. The strongest relationship was between strikeout rate and maximum velocity change – with a drop in peak fastball velocity explaining 23% of the variance in change in Strikeout Rate. Clearly, losing a couple of ticks off your fastball doesn’t help – the relationship between success and fastball velocity is pretty well established. That means that we shouldn’t necessarily be looking at a pitcher’s peak velocity – but also how their velocity changes as they pitch throughout a game. For the 2016 season, I looked at starts that lasted 5 innings, and pitchers who started at least 10 games. For the 2017 season, I only looked at the starters that have appeared in 3 games so far. These lists do look pretty consistent, year to year. Biggest Velocity Droppers, in game, for 2016 season Rank Name Average change in maximum pitch velocity, in game. 1 Rick Porcello -2.89 2 Colin Rea -2.48 3 Cody Anderson -2.37 4 Michael Feliz -2.30 5 Chris Devenski -2.22 6 Kendall Graveman -2.12 7 Mike Clevinger -2.06 8 Jarred Cosart -2.01 9 Paul Clemens -1.99 10 Phil Hughes -1.98 11 Juan Nicasio -1.97 12 Andrew Cashner -1.97 13 Mat Latos -1.94 14 James Shields -1.93 15 Tyler Wilson -1.91 16 Matt Shoemaker -1.80 17 Drew Pomeranz -1.74 18 Tyler Skaggs -1.74 19 Jesse Hahn -1.70 20 Jharel Cotton -1.70 SOURCE: PITCHf/x Biggest Velocity Droppers, in game, for 2017 season Rank Name Average change in maximum pitch velocity, in game. 1 Braden Shipley -3.50 2 Tyler Wilson -2.90 3 Jake Arrieta -2.80 4 Rick Porcello -2.66 5 Jeff Hoffman -2.50 6 Daniel Wright -2.50 7 Amir Garrett -2.50 8 A.J. Griffin -2.48 9 Mike Clevinger -2.43 10 Josh Tomlin -2.37 11 Drew Pomeranz -2.36 12 Mat Latos -2.30 13 Alex Meyer -2.30 14 Matt Harvey -2.30 15 Zach Lee -2.30 16 Austin Bibens-Dirkx -2.30 17 Patrick Corbin -2.28 18 Danny Duffy -2.22 19 Jayson Aquino -2.20 20 Matt Shoemaker -2.16 SOURCE: PITCHf/x There was a lot being made about Matt Harvey’s April velocity – but if you look here, he’s losing up to 2.3 mph off his fastball in his starts in 2017. That would explain how hard he has gotten shelled once he rolls into the end part of games. Perhaps this is a sign that Harvey is just lacking some of that in-game strength, and once his stamina comes back, he will start racking up those elite numbers again? Maybe? Come on? (I drafted him in way too many leagues. This is me trying to sound smart). All of this makes perfect sense. Velocity is a proven indicator of success; yes, you can be successful without it. You can successfully start a business without money; but money (and velocity) sure makes succeeding a lot easier.m The moral of the story – performance takes a significant hit when your pitcher loses velocity as they become fatigued. Whether or not this is a precursor for injury, at least, fatigue susceptibility, is another story. References Bruce, J. R., & Andrews, J. R. (2014). Ulnar collateral ligament injuries in the throwing athlete. Journal of the American Academy of Orthopaedic Surgeons, 22(5), 315-325.