The Z Files: The Fallacy of Stabilization and an Early Look at Home Runs

The Z Files: The Fallacy of Stabilization and an Early Look at Home Runs

This article is part of our The Z Files series.

One of the pitfalls of having more data to use is more people are misusing data. Something I have talked about previously is the notion of stability points and how they don't mean what many think they mean. Well, I'm back with my annual warning, but this time I've come with backup. In addition, I'll take a look at how the 2021 baseball is playing, which may not be as expected (or reported, at least very early).

STABILIZATION POINTS

A stabilization point is loosely defined as when a particular metric becomes useful. I'll spare the next level algebra, but one way to look at it is the stabilization point is the sample size where half is a result of the player's skill and half is happenstance.

When I first read about the concept many years ago, it appeared incorporating stabilization points in rest-of-season projections could be cutting-edge analysis. For example, the stabilization point for strikeouts is around 60 plate appearances. Imagine being able to make salient adjustments to player expectations only two weeks into the season! Yeah, pretty cool.

As an example, if I projected a 30 percent strikeout rate for a batter and after 60 trips to the dish, he was fanning 20 percent of the time, I'd adjust my rest-of-season strikeout rate to 25 percent. The idea is there was a 50/50 chance the players new 20 percent mark was real, so I averaged it with my initial projection. I framed the analysis as a guide to identifying

One of the pitfalls of having more data to use is more people are misusing data. Something I have talked about previously is the notion of stability points and how they don't mean what many think they mean. Well, I'm back with my annual warning, but this time I've come with backup. In addition, I'll take a look at how the 2021 baseball is playing, which may not be as expected (or reported, at least very early).

STABILIZATION POINTS

A stabilization point is loosely defined as when a particular metric becomes useful. I'll spare the next level algebra, but one way to look at it is the stabilization point is the sample size where half is a result of the player's skill and half is happenstance.

When I first read about the concept many years ago, it appeared incorporating stabilization points in rest-of-season projections could be cutting-edge analysis. For example, the stabilization point for strikeouts is around 60 plate appearances. Imagine being able to make salient adjustments to player expectations only two weeks into the season! Yeah, pretty cool.

As an example, if I projected a 30 percent strikeout rate for a batter and after 60 trips to the dish, he was fanning 20 percent of the time, I'd adjust my rest-of-season strikeout rate to 25 percent. The idea is there was a 50/50 chance the players new 20 percent mark was real, so I averaged it with my initial projection. I framed the analysis as a guide to identifying players likely to outproduce or fall short of their initial projection.

A couple years later, Russell Carleton, the developer of stabilization points, revealed we'd all been doing it wrong. In short, while the luck to skill ratio within the sample is evenly split, it isn't predictive of the next sample of the same size. Sticking with strikeout rate, the level of the first 60 plate appearances isn't indicative of the next 60, which is what I (and others) assumed. Oops.

Carleton did go on to say metrics with lower stabilization points should be reliable sooner than those with larger samples, so there is some utility. It's just not as fast-acting as suggested above.

I've explained this before and dropped the mic, so to speak. Not that everyone reads me or listens to me, but the preponderance of analysis akin to what I described illustrates many still have not received the memo. Stabilization points are by no means stable, nor are they predictive, reliable or even as useful as they are made out to be.

As such, I decided to look at the 2019 season and see what would have happened if I employed the 50 percent regression analysis. Every player with at least 240 plate appearances was included and broken into 60 plate appearance segments with the following calculated for each:

PROJ: Measures how far off I was projecting the K% for all the players within that pool compared to the final K%.

YTD: Assumes the newly projected K% is the mark at the end of the respective interval, then measures how far that is from the final K%.

50% Reg: Averages the K% at the end of each segment with the projected K% and compares to the final K%.

Weighted Average: Projects K% by using the K% at the end of each segment, then the projected K% for the rest of the season.

Admittedly, this is confusing, so here are some examples of each calculation.

PROJ: Player A has a projected 25% strikeout rate and finishes with a 30% clip. The math is (30-25)/30 = 16.7%, so I was 16.7% off. This is done for everyone within the sample. The absolute value is used to get the average "accuracy".

YTD: Player A has a 20% strikeout rate at the end of the interval. The math is (30-20)/30 = 33.3% and again done for everyone and averaged.

50% Reg: Using the data from YTD, newly projected K% is 22.5% (average of 20% and 25%). The math is now (30-22.5)/30 = 25%.

Weighted Average: Let's say I project Player A for 500 plate appearances and the 20% came at the end of the 60 PA interval. His new projected K% is ((60 x 20%) + (440 x 25%))/500 = 24.4%. The accuracy math is (30-24.4)/30 = 18.67%.

If you don't care about the math and are only interested in the results, the smaller the number, the better.

PA

PROJ

YTD

50% Reg

Weight Avg

60

12.90%

20.58%

12.51%

11.64%

120

12.90%

14.40%

9.63%

10.09%

180

12.90%

10.00%

8.27%

8.46%

240

12.90%

7.64%

7.37%

6.56%

300

12.59%

6.21%

6.94%

5.78%

360

12.22%

4.81%

6.58%

4.87%

420

11.91%

3.81%

6.37%

4.16%

480

12.20%

3.09%

6.17%

3.15%

540

12.49%

2.42%

6.21%

2.53%

600

13.92%

3.48%

7.81%

3.54%

The first point of interest is the 50% regression does not improve upon the initially projected K%. The sample doesn't provide useful data as discussed above. In fact, not only that, but the original K% is also far more accurate than the 60 PA sample when compared to the final mark.

Here I could drop the mic as I've shown the 50% regression doesn't accomplish what I once thought, and others still desire. However, looking at the larger samples, the 50% Regression was best at 120 PA before the weighted average took over. At around 360 PA, the best predictor of final K% was in fact the year-to-date K%.

For those into this sort of thing, the results are a little different year-to-year, but the general trend is close. I gathered this data for several metrics and have refined the regressions to fuel my rest-of-season projection engine.

The following is presented for entertainment purposes only, please no wagering. It's the monthly data for everyone eligible for the study. It's interesting to see some of the individual player trends and how misleading it would have been to utilize the 60 PA data to make decisions shaping your team for the next 24 weeks.

PlayerPAFinal K%60.0120.0180.0240.0300.0360.0420.0480.0540.0600.0660.0720.0
Marcus Semien74713.712.711.314.715.214.913.313.213.514.014.113.814.0
Whit Merrifield73517.19.814.014.714.818.417.418.217.016.916.116.217.0
Ronald Acuna71526.319.421.021.723.524.223.524.223.524.624.525.6 
Jonathan Villar71424.618.017.221.822.723.223.523.924.124.624.524.3 
Mookie Betts70614.319.415.414.214.714.314.014.214.514.714.914.7 
Rhys Hoskins70524.521.925.827.325.123.823.923.523.222.923.324.1 
Jorge Polanco70416.515.614.913.815.314.514.415.115.415.715.816.7 
Rafael Devers70217.017.716.415.216.416.816.315.615.715.716.216.7 
Ozzie Albies70216.011.116.915.817.015.216.016.116.616.116.316.1 
Eduardo Escobar69918.622.221.821.520.620.919.819.920.219.018.618.7 
Xander Bogaerts69817.519.021.118.619.318.917.418.218.318.517.817.9 
Pete Alonso69326.430.628.527.527.525.925.125.226.425.725.025.5 
Jose Abreu69321.927.923.624.524.523.423.022.722.422.021.822.2 
Freddie Freeman69218.410.918.417.716.816.217.218.418.317.918.218.2 
Alex Bregman69012.015.914.816.814.512.513.313.213.312.712.612.2 
Carlos Santana68615.79.416.414.616.516.314.915.314.914.715.015.2 
Bryce Harper68226.129.728.930.931.029.527.626.426.726.826.326.2 
Paul Goldschmidt68224.328.126.226.826.424.326.126.025.424.424.024.0 
Jorge Soler67926.236.937.031.330.629.029.327.626.726.726.126.2 
Trey Mancini67920.922.720.722.021.219.420.222.421.321.020.920.6 
Starlin Castro67616.419.414.812.113.915.817.417.016.716.616.816.4 
Matt Chapman67021.99.511.515.216.118.819.119.020.320.421.722.2 
Eric Hosmer66724.422.623.420.119.919.919.619.020.821.222.424.1 
Cesar Hernandez66715.011.513.213.713.113.513.313.813.914.214.715.0 
Paul DeJong66422.425.417.616.317.318.519.019.120.720.721.522.4 
Nick Castellanos66421.530.227.924.923.722.721.522.222.021.622.321.5 
Eugenio Suarez66228.524.223.626.623.625.226.627.327.928.328.728.5 
Nolan Arenado66214.07.98.311.010.711.913.013.413.513.313.514.0 
Manny Machado66119.424.225.821.021.920.920.620.119.819.819.719.4 
Cody Bellinger66116.311.311.313.812.915.014.715.616.116.816.116.3 
Shin-Soo Choo66025.023.019.723.924.924.724.023.424.024.324.7  
Josh Donaldson65923.521.526.627.228.326.225.324.924.624.724.3  
Juan Soto65920.027.425.625.123.321.721.321.619.719.919.9  
J.D. Martinez65721.09.413.714.216.018.619.419.620.219.920.2  
Trevor Story65626.526.626.024.526.727.426.426.225.725.225.5  
Adam Eaton65616.222.219.019.818.517.516.115.615.515.215.9  
Amed Rosario65518.929.723.625.824.423.422.320.820.419.919.4  
DJ LeMahieu65513.715.614.913.713.313.814.614.213.613.413.9  
Tommy Pham65418.816.115.216.417.818.219.319.418.918.818.2  
Francisco Lindor65415.022.616.913.715.915.816.815.914.714.714.3  
David Fletcher6539.84.74.16.56.27.38.29.49.19.89.1  
Michael Conforto64823.023.420.821.222.322.522.922.821.921.822.8  
Elvis Andrus64814.821.519.416.515.316.115.315.114.714.414.6  
Anthony Rendon64613.312.717.916.516.215.814.114.114.313.613.2  
Kevin Pillar64513.816.117.718.216.514.514.615.114.514.414.3  
Michael Brantley63710.414.511.49.98.79.09.810.410.510.310.4  
Kris Bryant63422.923.118.217.918.918.219.719.820.921.422.3  
Charlie Blackmon63416.417.717.715.918.317.918.218.017.317.216.6  
Alex Gordon63315.89.711.313.014.517.817.616.516.715.515.9  
Kole Calhoun63225.625.425.623.922.221.823.622.824.224.725.5  
Yasmani Grandal63222.022.221.322.020.520.921.520.920.721.422.2  
Randal Grichuk62826.031.728.528.028.229.228.527.727.326.426.2  
Ketel Marte62813.717.219.818.817.715.915.013.713.414.213.9  
Nick Ahmed62518.124.220.520.319.818.819.518.117.418.318.0  
Lorenzo Cain62317.011.118.216.315.917.117.717.316.717.217.1  
Leury Garcia61822.531.324.025.023.622.222.223.222.823.122.4  
Jean Segura61811.813.812.913.112.412.811.811.611.411.912.1  
Victor Robles61722.730.830.326.925.924.024.524.123.423.122.6  
Brandon Belt61620.625.425.021.720.319.519.620.720.220.520.7  
Andrew Benintendi61522.819.722.023.824.123.422.923.023.823.522.9  
Josh Bell61319.219.024.622.319.820.519.920.620.720.119.5  
Anthony Rizzo61314.020.316.114.213.913.614.715.115.114.414.1  
Yuli Gurriel61210.611.312.411.510.611.210.210.610.210.510.8  
Yasiel Puig61121.825.824.225.825.223.822.021.522.122.121.8  
Kyle Schwarber61025.631.126.226.527.027.927.925.524.925.425.3  
Joey Votto60820.224.627.023.622.722.520.520.220.720.220.3  
Adam Frazier60812.316.114.313.612.312.611.812.012.212.112.4  
Gleyber Torres60421.422.222.821.223.522.622.621.020.221.021.1  
Christian Walker60325.729.029.831.129.627.627.227.925.926.225.7  
Mike Trout60020.07.911.315.415.817.818.017.618.620.2   
Renato Nunez59923.925.827.627.725.325.625.425.124.022.7   
Max Kepler59616.620.317.914.815.615.515.615.815.916.2   
J.T. Realmuto59320.716.921.519.923.123.422.323.522.321.4   
Austin Meadows59122.221.920.719.924.223.924.424.823.722.8   
Eddie Rosario59014.617.715.214.714.914.213.513.914.114.9   
Max Muncy58925.328.629.826.425.923.723.824.524.225.2   
Freddy Galvis58924.621.522.822.824.322.722.922.823.624.5   
Jason Heyward58918.79.514.616.916.517.218.519.418.719.2   
Hunter Dozier58625.317.720.019.320.222.222.223.123.324.1   
Starling Marte58616.020.018.718.417.017.516.916.416.315.8   
Willy Adames58426.232.829.326.427.025.828.227.226.226.1   
Mike Moustakas58416.820.621.018.819.918.417.316.616.416.2   
Rougned Odor58130.635.934.733.331.732.831.731.331.330.7   
Christian Yelich58020.315.416.417.616.416.918.819.320.620.3   
Dexter Fowler57424.727.922.323.623.523.624.924.623.223.5   
Trea Turner56919.919.022.824.024.623.022.720.620.219.7   
Jackie Bradley56727.329.030.927.728.224.825.725.625.827.1   
Jeff McNeil56713.29.78.910.311.011.813.013.213.413.4   
Mallex Smith56624.728.627.926.725.724.423.825.525.325.0   
Javier Baez56127.832.826.829.131.830.329.028.928.027.3   
Brandon Crawford56020.927.425.625.423.023.823.422.921.021.0   
Yoan Moncada55927.522.622.427.728.828.127.328.027.527.7   
Daniel Vogelbach55826.723.822.322.721.422.424.024.625.826.0   
George Springer55620.331.325.821.621.420.120.319.820.220.7   
Yolmer Sanchez55521.127.725.823.622.322.822.221.721.221.0   
Brett Gardner55019.69.810.412.214.314.916.616.818.319.5   
Josh Reddick55012.012.710.712.211.010.010.511.411.411.7   
Hanser Alberto5509.112.711.410.59.09.59.49.28.99.2   
Marcell Ozuna54920.825.820.320.820.721.820.420.820.720.8   
Miguel Cabrera54919.727.027.023.220.919.720.819.620.319.9   
Justin Turner54916.022.618.515.213.315.015.916.916.416.3   
Kolten Wong54915.115.915.613.616.315.013.814.514.815.1   
Franmil Reyes54828.519.724.026.528.126.526.228.128.228.6   
Jose Altuve54815.014.516.915.314.515.914.614.715.115.0   
Matt Olson54725.224.229.125.324.824.924.824.924.725.0   
Orlando Arcia54620.022.223.020.419.820.119.920.519.420.0   
Dansby Swanson54522.819.721.819.318.719.620.220.021.922.8   
Albert Pujols54512.511.112.913.611.511.612.712.813.012.5   
Jose Ramirez54213.716.414.014.415.613.813.512.213.113.7   
Corey Seager54118.121.321.521.119.718.919.018.118.418.1   
Ryan McMahon53929.723.426.226.028.328.727.728.429.4    
Khris Davis53327.420.326.425.326.625.826.927.426.8    
Kevin Newman53111.721.015.413.312.012.312.411.611.4    
Avisail Garcia53023.625.426.423.422.922.822.823.624.2    
Jose Iglesias53013.217.514.513.314.914.613.713.713.5    
Adam Jones52819.116.414.813.615.717.317.018.018.6    
Miguel Rojas52611.84.811.512.012.412.312.312.312.0    
Wilson Ramos52413.29.819.718.215.914.813.212.612.4    
Nelson Cruz52125.121.327.328.229.227.727.126.226.3    
Christian Vazquez52119.422.216.118.519.318.919.020.019.5    
Brian Anderson52021.927.721.522.822.421.621.321.621.1    
Tim Anderson51821.019.719.719.619.421.820.220.921.0    
Jurickson Profar51814.514.513.213.614.115.215.215.115.0    
Joc Pederson51421.615.921.319.618.919.621.721.621.6    
Asdrubal Cabrera51420.023.823.623.221.223.823.421.620.5    
Vladimir Guerrero51417.719.419.018.619.117.617.717.017.4    
Jason Kipnis51117.215.617.716.916.216.916.016.817.6    
Luke Voit51027.823.424.824.622.425.626.728.127.1    
Evan Longoria50822.022.218.719.820.720.321.021.622.2    
Ryan Braun50820.727.923.022.723.122.322.320.820.4    
Domingo Santana50732.323.425.427.928.329.028.829.831.5    
Eloy Jimenez50426.629.530.629.529.227.826.926.926.7    
Colin Moran50323.319.024.825.423.525.024.023.023.4    
Scott Kingery50029.425.425.426.627.929.328.429.429.3    
Justin Smoak50021.216.422.019.018.519.519.820.821.1    
C.J. Cron49921.418.519.521.121.219.119.720.321.7    
Todd Frazier49921.233.325.621.318.922.822.322.021.0    
Mark Canha49721.524.620.918.519.120.322.922.621.4    
Hunter Renfroe49431.223.825.826.927.927.027.729.331.1    
Matt Carpenter49226.223.124.823.124.124.625.525.925.6    
Joe Panik4919.66.612.011.511.210.59.99.59.8    
Wil Myers49034.331.337.235.735.436.034.934.434.3    
Ji-Man Choi48722.222.219.421.420.721.120.721.022.2    
Edwin Encarnacion48621.214.319.218.518.518.521.621.521.3    
Ian Desmond48224.729.724.026.626.526.424.824.624.7    
Brian Dozier48221.827.027.027.925.424.123.422.621.8    
Omar Narvaez48219.125.420.219.818.220.519.118.719.1    
Robbie Grossman48217.819.418.219.016.915.516.617.917.8    
Ramon Laureano48125.629.728.129.327.426.526.827.125.6    
Kevin Kiermaier48021.726.217.721.021.021.120.520.9     
Daniel Murphy47815.510.913.914.914.915.115.815.1     
James McCann47628.821.923.822.425.128.928.528.8     
Niko Goodrum47229.223.823.425.126.327.829.328.8     
Nomar Mazara46923.016.417.419.618.220.622.422.7     
Nick Markakis46912.617.713.210.911.911.913.012.6     
Jorge Alfaro46533.141.035.533.932.733.934.434.3     
Teoscar Hernandez46433.024.628.728.830.730.431.333.3     
Jonathan Schoop46425.020.020.221.422.623.725.224.5     
Marwin Gonzalez46321.223.426.223.823.022.621.920.6     
Enrique Hernandez46021.119.025.025.723.122.421.920.6     
Eric Thames45930.534.935.233.532.832.030.730.8     
Brian Goodwin45828.221.024.024.224.124.927.126.8     
J.D. Davis45321.415.923.019.820.120.421.121.9     
Jarrod Dyson45219.016.119.819.619.818.518.819.0     
Yadier Molina45212.89.27.47.710.212.811.312.3     
Roberto Perez44928.335.529.826.829.027.628.728.5     
Aaron Judge44731.532.827.429.531.330.230.430.5     
Brandon Drury44725.342.236.129.329.527.225.325.4     
Raimel Tapia44722.429.529.326.125.423.721.821.9     
Gary Sanchez44628.025.427.327.526.627.927.127.7     
Buster Posey44516.019.415.715.214.816.315.216.2     
Adalberto Mondesi44329.825.427.927.925.526.927.929.1     
Kyle Seager44319.421.021.821.420.720.120.819.0     
Eric Sogard44214.37.812.114.413.614.114.314.4     
Manuel Margot44120.018.823.021.920.118.918.719.3     
Miguel Sano43936.235.542.739.135.834.636.435.8     
Robinson Chirinos43728.628.624.024.328.128.830.228.7     
Cavan Biggio43028.629.528.528.027.629.328.528.5     
Maikel Franco42814.39.29.812.011.612.013.314.4     
Andrelton Simmons4248.711.38.97.18.79.38.38.7     
Jake Bauers42327.220.023.125.826.325.925.127.2     
David Peralta42320.615.624.024.323.021.921.220.6     
Robinson Cano42316.321.922.219.918.617.516.516.3     
Garrett Cooper42126.122.220.823.025.526.227.026.1     
Dee Strange-Gordon42114.512.914.813.715.815.213.514.5     
Brandon Dixon42032.438.133.631.230.630.432.9      
Christin Stewart41624.825.024.223.121.623.624.2      
Chris Taylor41427.828.624.226.627.727.126.3      
Nick Senzel41424.423.824.824.022.922.023.2      
David Dahl41326.630.631.529.325.325.726.9      
Rio Ruiz41321.325.419.820.822.622.822.7      
Tony Wolters41116.512.715.413.715.715.416.0      
Rowdy Tellez40928.432.333.129.827.728.129.3      
Willson Contreras40924.923.428.224.926.425.225.2      
Delino DeShields40824.518.819.419.022.825.225.4      
Harrison Bader40628.831.729.529.027.426.027.9      
Luis Rengifo40622.925.025.421.623.722.421.5      
Anthony Santander40521.213.818.018.116.918.220.6      
Jose Peraza40314.424.619.514.814.515.115.2      
Melky Cabrera39710.311.112.112.610.010.310.2      
J.P. Crawford39621.031.725.023.122.622.021.1      
Stephen Piscotty39321.419.422.120.921.322.321.4      
Dwight Smith39220.916.116.919.620.620.719.6      
Travis d'Arnaud39121.726.223.021.323.121.621.8      
Jeimer Candelario38625.625.426.828.327.327.125.5      
Danny Jansen38420.628.626.024.020.221.120.2      
Jesse Winker38415.618.819.417.616.416.616.1      
Michael Chavis38233.225.426.031.933.233.133.5      
Neil Walker38120.223.823.421.722.220.220.3      
Alex Verdugo37713.012.510.79.910.69.612.7      
Martin Maldonado37423.016.420.522.121.122.822.7      
Jose Martinez37322.018.817.918.819.319.121.1      
Fernando Tatis37229.632.828.228.728.729.928.9      
Greg Garcia37222.319.425.023.123.723.022.7      
Ryan O'Hearn37026.821.022.822.724.626.226.6      
Chad Pinder37023.812.516.921.321.922.824.2      
Howie Kendrick37013.214.815.414.212.414.013.3      
Harold Castro36923.321.020.324.623.724.222.4      
Jesus Aguilar36922.016.421.823.523.121.121.5      
Logan Forsythe36727.221.322.024.326.625.727.1      
Carson Kelly36521.621.316.318.819.820.221.6      
Tucker Barnhart36422.821.325.426.823.823.822.9      
Curtis Granderson36327.034.432.828.027.227.527.0      
Albert Almora36317.114.815.615.814.916.217.1      
Stevie Wilkerson36129.932.830.932.032.032.229.9      
Mitch Garver35924.222.627.026.626.224.8       
Yan Gomes35823.526.626.023.222.122.8       
Ben Gamel35629.230.629.828.528.729.6       
David Bote35626.119.023.124.224.926.6       
Billy Hamilton35324.619.721.323.521.524.3       
Chris Davis35239.537.535.839.339.339.9       
Keston Hiura34830.733.832.330.930.729.9       
Austin Hedges34731.424.626.832.232.430.7       
Yandy Diaz34717.614.317.218.416.416.9       
Didi Gregorius34415.418.017.113.714.314.1       
Lourdes Gurriel34325.124.225.625.824.524.4       
AJ Pollock34221.612.321.021.421.221.7       
Josh Phegley34218.421.319.018.016.216.9       
Pedro Severino34121.427.019.820.719.822.5       
Willie Calhoun33715.717.719.017.516.615.5       
Mitch Moreland33522.123.422.120.821.221.9       
Yonder Alonso33520.917.718.221.021.120.1       
Matt Adams33334.535.533.131.132.633.3       
John Hicks33332.724.228.732.029.833.0       
JaCoby Jones33328.234.931.430.429.028.0       
Jay Bruce33324.629.529.829.324.423.8       
Elias Diaz33216.915.616.114.916.517.4       
Cheslor Cuthbert33020.327.924.822.319.920.3       
Tim Beckham32831.120.630.329.331.530.8       
Jonathan Lucroy32815.59.710.713.313.615.5       
Brandon Lowe32734.631.733.136.533.534.2       
Garrett Hampson32726.925.426.228.229.826.6       
Carlos Correa32123.423.827.223.823.822.9       
Tommy La Stella3218.76.56.68.08.38.6       
Jake Marisnick31829.931.730.627.729.330.1       
Hunter Pence31621.816.118.219.721.319.8       
Brian McCann31616.811.510.415.917.816.5       
Tyler Flowers31033.930.234.134.634.033.4       
Richie Martin30926.932.832.231.529.126.9       
Kurt Suzuki30911.716.116.513.812.412.0       
Derek Dietrich30624.219.420.222.422.624.6       
Gerardo Parra30119.620.619.020.919.919.6       
Joey Gallo29738.435.932.835.035.1        
Austin Riley29736.433.331.733.034.9        
Mike Tauchman29624.030.631.727.525.6        
Pablo Sandoval29622.626.226.024.923.4        
Ronald Guzman29529.532.828.626.029.9        
Byron Buxton29523.124.623.823.823.0        
Brock Holt29519.324.620.717.719.4        
Ronny Rodriguez29427.919.731.728.627.7        
Tyler Naquin29422.432.827.623.223.2        
Victor Reyes29221.921.322.022.022.4        
Mike Zunino28933.922.627.430.232.4        
Dawel Lugo28820.518.018.020.320.6        
Juan Lagares28526.328.627.629.026.7        
Jose Osuna28516.818.817.718.116.9        
Wilmer Flores28510.99.59.811.610.2        
Mitch Haniger28328.626.226.028.027.4        
Dylan Moore28233.038.130.934.232.0        
Tom Murphy28131.041.336.935.433.2        
Steven Duggar28127.831.728.927.528.4        
Ian Kinsler28119.218.017.217.117.7        
Stephen Vogt28023.629.026.223.823.0        
Tyler White27928.032.833.130.229.4        
Victor Caratini27921.119.723.122.521.0        
Corey Dickerson27920.119.717.118.218.1        
Tony Kemp27916.819.714.515.317.0        
Billy McKinney27626.423.421.124.926.1        
Jason Castro27532.024.225.026.931.4        
Jordy Mercer27121.021.019.019.120.9        
Travis Shaw27033.032.330.732.632.6        
Cameron Maybin26926.825.825.025.126.6        
Leonys Martin26429.529.730.629.830.2        
Joey Wendle26317.921.919.817.316.6        
Andrew McCutchen26221.027.725.221.421.3        
Jordan Luplow26123.432.827.925.724.0        
Phillip Ervin26024.234.424.223.825.3        
Martin Prado26015.812.715.615.815.2        
Justin Upton25630.527.029.530.430.7        
Greg Allen25620.725.821.019.520.3        
Aaron Hicks25528.227.025.226.127.2        
Brandon Nimmo25428.041.033.329.028.1        
Welington Castillo25129.521.927.328.329.3        
Tim Locastro25017.613.112.414.217.3        

EARLY SEASON HOME RUNS

It's rather remarkable how early-season home run data portends end of season results. It takes around 450 games to be predictive, but the home run rate (HR/PA) at that time very closely projects the ensuing monthly totals. Here is the data, based on the HR% after around 450 games from 2017-2019:

2019ProjectedActual
April3.43%3.44%
May3.73%3.58%
June3.83%3.67%
July3.86%3.74%
August3.85%3.85%
September3.59%3.55%
Final3.82%3.63%
2018ProjectedActual
April2.76%2.82%
May3.00%3.09%
June3.08%3.07%
July3.11%2.99%
August3.09%3.15%
September2.89%2.98%
Final3.07%3.02%
2017ProjectedActual
April2.97%3.08%
May3.22%3.28%
June3.31%3.53%
July3.34%3.25%
August3.32%3.47%
September3.10%3.14%
Final3.30%3.29%

It's not perfect, but the correlation between projected and actual is pretty strong. At minimum, it points us in the right direction. It should be noted weather/temperature is a factor as Opening Day was a few days earlier in 2019, and even though we're only dealing with three or four games per team, when the data is prorated based on just 15 games per team, that's 20-25% of the sample.

Through Friday's games, we're a little less than halfway to the 450 games target, as 212 games are in the book. If the typical pattern holds true, we're looking at a new MLB home run record. Even in April, as the weather warms in the Midwest and Northeast, power increases and so far, homers are ahead of the pace from 2017-2019. That is, the expectation is the already elevated mark will grow even more, to the points the home run rate after 450 games will eclipse the totals from the prior three years. Here is some of the relevant data, using a similar number of games from 2017-2019:

Key

  • Ave FB Dist: Average Fly Ball Distance
  • AEV FB: Average Exit Velocity on Fly Balls
  • HR%: HR/PA
YearGamesAve FB DistAEV FBHR%
2021212319.692.93.03%
2019202317.691.92.93%
2018201315.592.42.77%
2017206318.391.42.90%

At the same points of the season, at least in terms of games, 2021 is pacing ahead of 2019. So much for the softer, less bouncy baseball.

While it's too early to draw definitive conclusions, perhaps the fact the ball is 2.8 grams lighter counteracts the lower coefficient of restitution. A lighter object struck with the same force should incur a higher exit velocity.

There are other factors influencing the flight of the ball, notably air resistance. The narrative is fly balls aren't carrying, but the early data indicates otherwise.

I'll revisit this data in a couple weeks, when the sample is actionable.

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ABOUT THE AUTHOR
Todd Zola
Todd has been writing about fantasy baseball since 1997. He won NL Tout Wars and Mixed LABR in 2016 as well as a multi-time league winner in the National Fantasy Baseball Championship. Todd is now setting his sights even higher: The Rotowire Staff League. Lord Zola, as he's known in the industry, won the 2013 FSWA Fantasy Baseball Article of the Year award and was named the 2017 FSWA Fantasy Baseball Writer of the Year. Todd is a five-time FSWA awards finalist.
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