2022 Projection Accuracy: Hitter Playing Time by Jeff Zimmerman February 3, 2023 What started as a checkup on how projections turned into a fairly important find when using projections. On the projection front, aggregators, especially when done smartly, continue to crush the competition. The big illumination is ZiPS being near the top since it uses zero human input. First off, here are last season’s results with my conclusion. Hitter Playing Time For playing time, three of the aggregators, Average, ZEILE, and ATC shoved in this category (Depth Charts takes a hit because it only uses one playing time input). It’s an easy win for the Wisdom of the Crowds. To find this year’s player set to test, I used all the hitters drafted in at least 42 of the 47 NFBC Main Events. From this list, I excluded Seiya Suzuki because several systems didn’t include him. Also, I excluded Nelson Cruz, Luis Garcia, Manuel Margot, Jake Fraley, Seth Brown, Garrett Cooper, and Darin Ruf 러프. One or multiple systems didn’t have a projection for them. In all, I would have removed four projections, but decided it was better to have more projections and a few players missing. In all, this process was run on 223 hitters. With the same dataset, I removed the hitters who missed a significant part of the season due to injury. The players left out were Adalberto Mondesi, Miguel Sano, Alex Kirilloff, Kris Bryant, Austin Meadows, Anthony Rendon, Ozzie Albies, Jazz Chisholm Jr., and David Fletcher. To determine accuracy, I calculated the Root Mean Square Error (RMSE) for four different sets of values. RMSE is a “measure of how far from the regression line data points are” and the smaller the value, the better. I collected the projections on April 6th from a mix of 23 different sets. Some were free while others were behind a paywall. Those behind a paywall will be labeled as Paywall with a number (e.g. Paywall #1). Additionally, some of the projections were aggregates of other projections. All but one of the aggregators were publicly available. The one that wasn’t is called Aggregator #1. ATC, Depth Charts, and ZEILE are the projections that aggregate their competitors. Also, Steamer, ZiPS DC, and Depthcharts use the same playing time projections. THE BAT and THE BAT X use the playing time from ATC. Finally, I looked into several ways to aggregate the projections to see if there was a preferred method and they were: Average of all Median of all Preseason smart average: For this one, I had Rob Silver look at last season’s results, pick three sources to average, and they were used. He chose THE BAT X, Razzball, and Paywall #6. Post-season best average: This started with an average of nine of the projections that I know get regular updates during the preseason. Next, I removed the worst remaining system using this year’s results. The value needed to get under 130.9, the top value for a standalone system. Here are the results. RMSE Value as Worse Systems Are Removed Systems RMSE 9 134.0 8 133.4 7 132.9 6 131.8 5 130.7 4 129.4 3 128.8 2 131.9 1 130.9 The three systems that had the best results are publicly available, Razzball, ZiPS, and Davenport. With all that out of the way, here are the rankings using the full 223 hitters. RMSE Values: All Players System RMSE Post-Season Best 128.8 Aggregator #1 130.8 Davenport 130.9 ZiPS 132.4 BatX 133.5 Bat 133.6 ATC 134.3 Preseason Guess 135.1 Mr.Cheatsheet 135.3 Median 136.0 Razzball 136.2 CBS 137.8 ZEILE 137.8 Paywall #2 138.6 Average 139.3 Paywall #5 140.0 DraftBuddy 140.1 FreezeStats 142.6 Paywall #3 142.7 Steamer 142.8 Paywall #4 143.1 DepthCharts 143.9 Paywall #6 143.9 Paywall #1 144.8 ZiPS DC 145.1 Rotoholic 169.4 Mays Copeland 173.4 Before drawing any conclusions, here are the results without the hurt players. RMSE Values: Hurt Players Removed System RMSE Post-Season Best 112.1 Davenport 113.6 Aggregator #1 115.2 THE BAT X 115.5 THE BAT 115.6 ATC 116.1 Mr. Cheatsheet 116.9 ZiPS 117.4 Median 117.5 Preseason Guess 117.6 Average 118.5 CBS 119.3 ZEILE 119.3 Razzball 119.8 Draft Buddy 120.9 Paywall #5 121.9 Paywall #3 123.1 Paywall #2 123.6 FreezeStats 124.1 Steamer 124.3 DepthCharts 124.8 Paywall #4 125.4 ZiPS DC 126.0 Paywall #1 126.3 Paywall #6 127.2 Rotoholic 150.3 Mays Copeland 157.8 Like last season, the aggregated systems (e.g. ATC, THE BATs, Median, ZEILE) are near the top. The two projections that stand-alone are Davenport and ZiPS. Last season, they didn’t perform horribly but not good enough to stand out . Here are those rankings. Note: I might be talking about Mr. Cheatsheet next year as a projection to target. Both of them had a bad finish but they both were near the top at other times. For standalone playing time projections, they should be given consideration along with the aggregators and Razzball. Since the playing time from ZiPS is separate from the other playing time projections here at FanGraphs, I asked Dan Szymborski, how he sets the playing for ZiPS. So setting playing time by just knowing player traits is at least average and outperforms most projection systems. I was not surprised to find that some of the factors helped predict playing time. While the short 2020 season has caused some hiccups, I found playing time projections could be improved by knowing a hitter’s previous playing (injuries), player talent (good players play more than crappy players), and age. My formula was just a 10% improvement, but still helpful. What ZiPS is doing is pointing out factors analysts might be missing. For example, why does ZiPS have Gunnar Henderson at 557 AB and Steamer down at 531 AB? A system must be even lower on Henderson’s playing time and is dragging ATC down to 510 AB. One issue with ZiPS is that it doesn’t robotically zero out playing time. There will be more plate appearances than available in a season. It’s not close to a perfect projection system, but it is definitely catching some factors other projections aren’t. Here are a couple of issues I could see chopping into ZiPS’s high rank going forward. It could just be a recent blip where analysts are still having problems evaluating playing time so near to the shortened 2020 season and the 2021 late start. Once baseball gets back to normal, analysts might perform better. The other projection creators could start spotting their biases and make adjustments to correct them. I’m not sure about this change happening. I discussed ZiPS’s performance with two people behind the better projections and they blew off the ZiPS results. It’s always I ton of work to set up these projection comparisons. As expected, the aggregators dominated again with a couple of single systems (ZiPS and Davenport) taking a step up this past season. It’s interesting that ZiPS performed as well as it did considering it has no human input.