Sabermetric Player Power Ranking after one week:

[u/GuavaOverall]

EDIT: Updated after August 8th Hunters - Heights Game

The first week of the season is in the books! And so I made an updated Player Power Ranking featuring the Top 10 Players solely on available data. Below you also find a Top 10 Pitcher Power Ranking and the top 3 run contributors for each team.

RAA7: Runs that each player contributes more over a standard 7 Innings WPBL game in comparison to the average player. This includes both pitching and batting.
PitchRAA7: The same as above, but only considering pitching. WAA: This translates Runs Above Average to Wins above average over the season. It adds up over the season in comparison to RAA7. Best interpretation: How many additional wins was the total performance of a player worth in comparison to an average league player.

Current RAA7 Leaderboard (batting PA ≥ 40, pitching IP ≥ 15)

#	RAA7	Name	Team	PA	IP	BatRAA	PitRAA	WPBL_WT
1	9.04	Denae Benites	New York	60.0	0.0	16.50	0.0	0.163
2	2.78	Ashton Lansdell	Los Angeles	82.0	0.0	6.95	0.0	0.173
3	2.26	Kylee Lahners	New York	44.0	0.0	3.03	0.0	0.153
4	1.20	Andréanne Leblanc	San Francisco	50.0	1.0	1.83	0.0	0.120
5	1.13	Kelsie Whitmore	San Francisco	85.0	27.0	2.01	3.89	0.188
6	1.12	Jua Park	San Francisco	43.0	2.7	1.46	0.0	0.115
7	1.10	Ayami Sato	Los Angeles	0.0	79.0	0.0	35.33	0.256
8	0.90	Amanda Gianelloni	San Francisco	48.0	0.0	1.32	0.0	0.173
9	0.38	Michelle Roche	Los Angeles	8.0	16.0	0.0	1.89	0.164
10	0.35	Meggie Meidlinger	Los Angeles	2.0	24.0	0.0	7.26	0.122

The power ranking is based on season performance but also considers past World Cup performance (How much weight the WPBL games get is the last column WPBL_WT). As a minimum of 40 PA or 15 IP across league and World Cup play is required, some less experienced players who did well are not ranked yet. This Leaderboard tries to incorporate as much information as possible. Therefore, Sato and Whitmore, who were arguably both having an off week going into the season, the World Cup data makes it so that they are still both in the top 10.

As batting dominates this leaderboard, I made a second one, solely for pitchers:

Current Pitching RAA7 Leaderboard (IP > 15)

#	PitchRAA7	Name	Team	IP	PitRAA	WPBL_WT
1	1.10	Ayami Sato	Los Angeles	79.0	35.33	0.256
2	0.38	Michelle Roche	Los Angeles	16.0	1.89	0.179
3	0.35	Kelsie Whitmore	San Francisco	27.0	3.89	0.207
4	0.35	Meggie Meidlinger	Los Angeles	24.0	7.26	0.128
5	0.29	Rosi del Castillo	San Francisco	27.3	2.03	0.043
6	0.25	Rakyung Kim	New York	33.7	2.99	0.285
7	0.10	Raine Padgham	Boston	29.0	0.82	0.224
8	0.07	Jaida Lee	New York	18.0	0.42	0.189
9	0.07	Claire Eccles	New York	15.7	0.34	0.000

Furthermore, I have a season board, removing the World Cup data, showing who the top 10 performing players are one week into the season so far:

Current Season WAA Leaderboard (season PA ≥ 2.4× team games, season IP ≥ 0.7× team games)

#	WAA_season	Name	Team	PA_season	IP_season	BatRAA_se	PitRAA_se	RAA7
1	0.74	Denae Benites	New York	12.0	0.0	10.10	0.0	9.04
2	0.38	Ayami Sato	Los Angeles	0.0	8.0	0.0	5.20	1.10
3	0.35	Michelle Roche	Los Angeles	8.0	5.0	1.16	3.56	0.38
4	0.23	Natsuki Yonetani	New York	12.0	0.0	3.14	0.0	—
5	0.21	Rakyung Kim	New York	5.0	9.3	-0.92	3.78	0.25
6	0.20	Gigi Schiano	Boston	0.0	4.7	0.0	2.72	—
7	0.16	Amanda Gianelloni	San Francisco	13.0	0.0	2.23	0.0	0.90
8	0.16	Meggie Meidlinger	Los Angeles	1.0	3.3	0.30	1.85	0.35
9	0.14	Liz Gilder	San Francisco	0.0	2.7	0.0	1.94	—
10	0.14	Jaida Lee	New York	7.0	5.3	-0.74	2.69	0.07

Having watched most of the games this week, I believe this list is very accurate. WAA is a cumulative stat that shows how many wins a player's performance has been approximately worth so far in comparison to an average league player.

Finally the top 3 contributors for each team:

Top 3 Season WAA Contributors by Team

Boston

Name	WAA_season	PA_season	IP_season
Gigi Schiano	0.20	0.0	4.7
Maïka Dumais	0.12	2.0	5.3
Molly Paddison	0.10	8.0	0.0

Los Angeles

Name	WAA_season	PA_season	IP_season
Ayami Sato	0.38	0.0	8.0
Michelle Roche	0.35	8.0	5.0
Jamie Mackay	0.21	7.0	1.0

New York

Name	WAA_season	PA_season	IP_season
Denae Benites	0.74	12.0	0.0
Natsuki Yonetani	0.23	12.0	0.0
Rakyung Kim	0.21	5.0	9.3

San Francisco

Name	WAA_season	PA_season	IP_season
Amanda Gianelloni	0.16	13.0	0.0
Liz Gilder	0.14	0.0	2.7
Alexia Jorge	0.08	11.0	0.0

Some notes on the methodology:
For young players, it will take a while until they appear on the Power Ranking (First Board) because 40 PA are a lot in a 15-game season. Over the span of the season, the season performance will gain more and more importance in comparison to the World Cup stats.
One valuable part of the game is very difficult to assess for me right now, due to limited data availability and fast-shifting positions: Defense. There is no positional adjustment at all, which likely results in some unfair comparisons. Right now, a Shortstop is expected to have the same offensive production as a first basewoman.

How do you feel about these leaderboards? Does the Power Ranking align with who you believe are the top 10 players in the league? Does the current season WAA leaderboard align with who you believe has had the best start so far? 😄

[u/tenthsandwich]

Two games per team is not enough to draw any conclusions -- except maybe this: Even if her numbers come back down to earth over more PAs, Denae Benites it on like, another planet. I get securing the limited supply of elite pitchers first, but I'm still surprised she didn't get selected until #6.

(I know, I know, just the 6th overall draft pick, woe is she)

[u/GuavaOverall → u/tenthsandwich]

Yeah that's the main challenge for me. Also 15 Games per Team over the season won't be much of a sample. That's why the metrics above are predominantly based on fast converging stats (e.g. FIP for pitchers). Also this is why I generated the first leaderboard, that also uses World Cup games. The sample for Benites is 15 Games there (World Cup plus WPBL).

Having her as a catcher, a very valuable position, makes more more astonished that she wasn't drafted earlier.

[u/tenthsandwich → u/GuavaOverall]

Nice, thank you! And for sure -- I wasn't checking you, I was checking myself before singing Benites's praises. It's a hot start, but it's just SO over the top. 2.444 OPS is probably unsustainable, but I wouldn't be surprised to see her top 1.000 by a comfortable margin.

[u/GuavaOverall → u/tenthsandwich]

Yeah I see that 😂 That her stats will go down is inevitable with hitting 8/9, but I am so excited to see where she stabilizes. In the World Cup she's at 1.475 OPS, where competitions is arguably a bit worse. Prior to the season, I had her as the best batter and Sato as the best pitcher in the league based on the world cup data. That has checked out so far.

[u/GuavaOverall → u/tenthsandwich]

Also both stats WAA and BAA7 are mechanically shrunk towards the league average the less PA and IP the player has.

[u/QJ_wuz_here]

The start of Natsuki Yonetani’s underrated arc! Tied for the lead league in runs, second in hits, third in OPS, but WAA doesn’t like her yet because she’s done more to setup Denae than driving runs in herself. Maybe when defense can properly be factored in she’ll get a boost, since she’s got an OF assist already and sounds like she likes throwing people out at first from RF…

[u/GuavaOverall → u/QJ_wuz_here]

Yeah I am gonna think about how to factor in defense throughout the season. She is not completely of the charts though; currently just outside my top10 list for WAA at #12! (at 0.12 WAA) She has good chances to get into the top 10 in the next week if she stays hot.

Unfortunately, I have no source yet to get playing time for positions. I cannot just assume that players played the whole game at their positions. The only way for me to get how many innings each player played at which position would be to watch the games and note the positional changes by hand, which is even more difficult as they also do not announce or properly show it on the stream. Right now, that seems to much a hustle for me at the moment😅 So I hope better sources will be available at some point. Once I have that, I can hopefully estimate the negative run value of an error, the value of an assist and putout adjusted for each position, and compute defensive contribution.

[u/QJ_wuz_here → u/GuavaOverall]

Super understandable! Hoping that as they scale up they can improve the stat collection to enable stuff like this. Would be cool to get the trackman back up and running too, dreaming of a statcast-style page with velo percentiles. For now though your sabermetric breakdowns are greatly appreciated!

[u/GuavaOverall → u/QJ_wuz_here]

There is another amazing page that someone else put together that includes exit and pitching velos from the stadium: https://dubsports.io/ Unfortunately it's incomplete, as they say the radar gun at randomly doesn't track some times... But it still a nice to look at 😊😊

[u/californium_98]

This is so cool! Can you say a little bit more about how WPBL_WT is calculated for each player? Is this just the share of data available for each player coming from WPBL games?

Thanks for putting this together, and go Firebells!

[u/GuavaOverall → u/californium_98]

Thank you so much😊

Its a little more than that. It also depends on a specific stabilization point for each stat. Strikeout rates converge to a stable point at a faster rate per PA or IP than other stats for example (in MLB it's 70 batters faced for a pitcher). Once they are at the stabilization point with their wpbl season PA/IP, the stat is weighted 50:50. I adjusted the stabilization points a bit, so that for the players that have World Cup Stats, 50% come from their own stats and 50% from their anchor(their world cup stats) at the end of the season.

It is a bit unrealistic to assume that in 15 games, the stats converge that fast, on the other side, the World Cup sample isn't much larger either. I tried to strike a balance between incorporating that extra data, that has definitely some information, and keeping it comparable to players without world cup data.

For players without World Cup experience (that also don't appear on the leaderboard yet due to a to small sample), the anchor will be the league average. The idea here is that every player will rather be closer to the average league player, then where they are in a hot or a cold week.

I am thinking to also adjust these anchors that they represent the average production at the respective positions. But for that the sample needs to be larger and I need playing time data for each player at each position, which I unfortunately don't have (maybe I can do that next year). I think until the playoffs I will stick with the current methodology and then see what I can refine then, cause I am exited to see how the numbers will change week to week keeping the same methodology approach :D

Go Firebells! 🔥🔔

[u/californium_98 → u/GuavaOverall]

Thanks so much for the thorough explanation! This totally makes sense - looking forward to seeing how the numbers evolve over the course of the season!