Replacement Player Analysis Using Adds & Drops
In most weekly leagues, the ability to add and drop players is gone for this season. Since there are no more moves, I’m going to analyze the most added and dropped players in NFBC’s Main Event and Online Championship with the main goal to create a composite replacement-level player.
For reference, the Online Championship (OC) leagues have 12 teams while the Main Event (ME) has 15. Both of the leagues require 23 starters each week with 7 bench spots (no IL spots). At all times, 360 players will be rostered in an Online league and 450 in a Main Event league. The reason I decided on the two NFBC formats were:
- The data is freely available.
- The information is from several leagues (43 Main Events, 199 Online Championships) with the same ruleset.
- The leagues remain competitive longer since there is decent money on the line.
- With two formats (12-team and 15-team), a comparison can be done on the different player pools.
I know at times we may seem a little NFBC centric here at Rotographs. Now, if some other platform had the ability to select a league type and make available all the adds and drops, I’d use them. The NFBC is the only platform that offers this service.
With the background stuff out of the way, here are the 30 players who were dropped and added the most in the two formats.
Name | Adds and Drops |
---|---|
Michael Fulmer | 948 |
Brandon Belt | 945 |
Austin Gomber | 911 |
Johnny Cueto | 879 |
Alex Cobb | 875 |
Daulton Varsho | 866 |
Luis Arraez | 837 |
Miguel Rojas | 832 |
Lucas Sims | 812 |
Logan Webb | 790 |
Jon Gray | 765 |
Gregory Soto | 754 |
Joe Ross | 753 |
Joakim Soria | 749 |
Rich Hill | 743 |
Jorge Alfaro | 737 |
Madison Bumgarner | 716 |
Triston McKenzie | 709 |
Drew Smyly | 707 |
Tony Gonsolin | 707 |
Kwang Hyun Kim 김광현 | 706 |
Lorenzo Cain | 688 |
Garrett Cooper | 684 |
Alejandro Kirk | 682 |
Cole Irvin | 681 |
Tarik Skubal | 676 |
Elieser Hernandez | 675 |
Nico Hoerner | 671 |
Harrison Bader | 669 |
Max Stassi | 667 |
Name | Adds and Drops |
---|---|
Michael Fulmer | 273 |
Wilmer Flores | 259 |
Pete Fairbanks | 259 |
Kevin Pillar | 248 |
Rowdy Tellez | 243 |
Nick Ahmed | 242 |
Rougned Odor | 241 |
Jose Iglesias | 240 |
Chad Green | 237 |
Brad Keller | 237 |
Leury Garcia | 229 |
Erick Fedde | 227 |
Yonathan Daza | 221 |
Josh Fleming | 220 |
Greg Holland | 218 |
Michael Wacha | 217 |
Chas McCormick | 216 |
Adrian Houser | 212 |
Kevin Kiermaier | 210 |
Edward Olivares | 210 |
Victor Caratini | 208 |
David Price | 208 |
Taylor Widener | 207 |
Devin Williams | 206 |
Ryan Weathers | 201 |
Niko Goodrum | 201 |
Asdrubal Cabrera | 201 |
Joe Ross | 199 |
Amir Garrett | 199 |
Here are some thoughts on the two lists.
- Going through the players, there are four common threads with the top names matching multiple criteria, multiple times. The first is being just OK. None of these players were good enough to keep rostered if hurt (the second variable) with available comparable replacements. The third factor is being a reliever who looked to be in line for Saves The final factor is a player with a high variance in opponent or ballpark quality such as anyone on the Rockies.
- Michael Fulmer leads both lists. He meets two of the common criteria for being dropped, a possible closer and being hurt.
And now for the meat of the article. For non-catcher hitters, starters, and relievers, I averaged the stats for the 12 (OC) and 15 (ME) most added and dropped players. These players were readily available on the waiver wire and most managers would be able to add them for little to no cost (i.e. replacement-level player). Additionally since several of these players were on the IL at various times, I standardized the playing time (600 PA for hitters, 150 IP for starters). To start with, here are the hitters.
15-team | PA | HR | R | RBI | SB | BB% | K% | AVG | OBP | SLG | OPS | BABIP |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Average | 362 | 9.2 | 42.5 | 40.5 | 3.7 | 7.9% | 22.3% | .243 | .312 | .389 | .701 | .292 |
Median | 351 | 9.0 | 42.0 | 42.0 | 2.0 | 7.6% | 22.0% | .241 | .313 | .385 | .698 | .289 |
Average | 600 | 15.3 | 70.5 | 67.1 | 6.1 | 7.9% | 22.3% | .243 | .312 | .389 | .701 | .292 |
Median | 600 | 15.4 | 71.8 | 71.8 | 3.4 | 7.9% | 22.3% | .243 | .312 | .389 | .701 | .292 |
12-team | PA | HR | R | RBI | SB | BB% | K% | AVG | OBP | SLG | OPS | BABIP |
Average | 405 | 10.6 | 50.4 | 43.6 | 6.0 | 9.6% | 19.2% | .266 | .344 | .420 | .765 | .312 |
Median | 420 | 9.0 | 54.5 | 44.0 | 6.5 | 9.8% | 19.9% | .269 | .342 | .405 | .746 | .310 |
Average | 600 | 15.7 | 74.8 | 64.6 | 8.9 | 9.6% | 19.2% | .266 | .344 | .420 | .765 | .312 |
Median | 600 | 12.9 | 77.9 | 62.9 | 9.3 | 9.6% | 19.2% | .266 | .344 | .420 | .765 | .312 |
There are a few points to take from the differences. Home run power can be found readily on the wire in both formats, but it’s a lot harder to find batting average (20 point difference) and steals (3 to 6 steal difference). Throughout the entire draft, it might be best to focus on batting average knowing it’ll be difficult to find during the season.
Here are the starter stats.
15-team | IP | SO | W | K/9 | BB/9 | HR/9 | BABIP | GB% | WHIP | ERA |
---|---|---|---|---|---|---|---|---|---|---|
Average | 115 | 95 | 6.4 | 7.4 | 3.2 | 1.4 | .292 | 45% | 1.37 | 4.76 |
Median | 123 | 109 | 7.0 | 7.6 | 3.2 | 1.5 | .290 | 44% | 1.33 | 4.62 |
Average | 150 | 125 | 8.4 | 7.4 | 3.2 | 1.4 | .292 | 45% | 1.37 | 4.76 |
Median | 150 | 133 | 8.5 | 7.6 | 3.2 | 1.5 | .290 | 44% | 1.33 | 4.62 |
12-team | IP | SO | W | K/9 | BB/9 | HR/9 | BABIP | GB% | WHIP | ERA |
Average | 122 | 116 | 7.4 | 8.8 | 3.1 | 1.2 | .283 | 42% | 1.25 | 4.03 |
Median | 120 | 116 | 7.5 | 9.0 | 2.9 | 1.2 | .286 | 39% | 1.24 | 4.13 |
Average | 150 | 143 | 9.2 | 8.8 | 3.1 | 1.2 | .283 | 42% | 1.25 | 4.03 |
Median | 150 | 145 | 9.4 | 9.0 | 2.9 | 1.2 | .286 | 39% | 1.24 | 4.13 |
If just one item can be taken away from this analysis, it is in the above information. In 12-teamers, pitchers with 4.00 ERA’s and 1.25 WHIP’s can be found to stream against weaker opponents. Those ratios jump to over a 4.50 ERA and ~1.35 WHIP in 15-teamers. There was no way to stream pitchers in the Main Event. If anyone popped up, they were kept. Also, it was way easier to find strikeouts (8.8 K/9 vs 7.4 K/9) in the 12-teamers.
And finally closers. I was going to adjust the closers to 60 IP, but they were already there.
15-team | IP | SO | W | SV | K/9 | BB/9 | BABIP | GB% | WHIP | ERA |
---|---|---|---|---|---|---|---|---|---|---|
Average | 60 | 68 | 4.9 | 6.2 | 10.3 | 3.3 | .291 | 43% | 1.20 | 3.39 |
Median | 60 | 71 | 5.0 | 6.0 | 10.5 | 3.3 | .296 | 42% | 1.17 | 3.02 |
12-team | IP | SO | W | SV | K/9 | BB/9 | BABIP | GB% | WHIP | ERA |
Average | 60 | 70 | 4.8 | 9.3 | 10.7 | 3.9 | .294 | 41% | 1.27 | 3.68 |
Median | 62 | 73 | 5.0 | 9.0 | 10.8 | 3.3 | .293 | 43% | 1.34 | 3.77 |
The difference here is that in the 12-teamers, managers had more options for Saves and rostered them. In the 15-teams, managers focused on ratios more and just hoped the Saves would come along.
When possible, it’s important to know what can and can’t be added in various league types. This analysis is especially important for “experienced” fantasy managers who may have been used to a certain player being available in the good old days (or in other leagues). While the game and the people playing it change every season, the best estimate going forward would be from the previous season.
Jeff, one of the authors of the fantasy baseball guide,The Process, 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 four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR twice, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.
Nice article. Useful information and book marking for next years main and ocs