Just tweeting about numbers and sports. I probably say dumb things once in a while. please ignore them.
Joined May 2020
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Sports and Math retweeted
I use ESPN EPA data (feel it does a good job dividing credit vs naive EPA models) but it only goes until 2006 so might need to do something similar to 538/nfelo
nba-ranking-app.vercel.app/n…
also liked looking to the “team efficiency” as a PGWE type metric
then WP-based leverage
Sports and Math retweeted
Overall:
1. Mahomes vs 2. Allen:
- I have Mahomes > Allen every single year of his career, but it's been well within the margin of error each of the past 6 years (2021-2026), with it being closest in 2023 (but honestly '24, '26, '22, '25, even '21 are incredibly narrow margins)
Sports and Math retweeted
🏈 cfbfastR 3.0.0 is on CRAN — the biggest release in the package's history, and it lands for Week 1.
• ESPN college football layer: 8 → 73 wrappers (players, teams, games, pbp, FPI, QBR, recruiting, futures) a Fox Sports layer (fox_cfb_*)
• Rebuilt modular play-by-play EPA/WPA engine — cfbd_pbp_data_v2() and espn_cfb_pbp_v2() run one shared engine w/ penalty resolution, player ids, and output tiers
• 48 new full-season data loaders covering every published release dataset: 27 ESPN datasets (2004 ), ratings/talent/recruiting/crosswalks, and 10 stats.ncaa.org datasets — including FCS play-by-play
• 15 new CFBD endpoints (playoffs, coaches, ratings, player success), situational params everywhere, betting ATS records, per-game havoc
• httr2 HTTP layer, native pipe throughout, session caching for ESPN catalogs
Nothing deprecated — your old code keeps working, with three pbp sources to choose from (classic 2014 , ESPN 2004 , NCAA incl. FCS 2013 ).
install.packages("cfbfastR") 📖 cfbfastR.sportsdataverse.org
Every one of those loaders has a twin in sportsdataverse-py reading the same published files — the R and Python loader surfaces were built to mirror each other.
One caveat on the EPA/WPA models if you build play-by-play yourself this weekend — next post. 👇
Sports and Math retweeted
Here is a Google spreadsheet with 10 years of individual
- box-score
- PlayByPlay
- RAPM
and other advanced data
For those who want to dabble in building their own NBA advanced player metric, similar to BPM
docs.google.com/spreadsheets…
teapawt.net/JerryEngelmann/status/…
Part 6 of our (free) analytics series:
How to build an NBA box-score metric
roycewebb.com/p/how-to-build…
Sports and Math retweeted
eh I think that's overstating it to make the case LEBRON is King
[self-promo] check out my website's MVP page (customizable based on 3 factors: Impact, Durability, Winning) - nba-ranking-app-282129715603…
Sports and Math retweeted
Interesting take
Here’s my version of that chart based on stats instead of awards to quantify how good each player’s teammates were
Sports and Math retweeted
Peak for Peak,
2013 (or 2019) Paul George was a bit better
but Jimmy's been better than PG the last 6 years (when PG was a Clipper/Sixer and Jimmy was on the Heat/Warriors)
Definitely huge rivals in the middle of their careers,
but young PG > young Jimmy
old Jimmy > old PG
Sports and Math retweeted
If we only filter to All-NBA caliber players
It’s nice to see LaMarc Gasoldridge makes the Top 30 Rivalries
Sports and Math retweeted
So this gave me the idea to look at something similar to six degrees of separation
where you create a graph between players with each crossing making them closer
and then you can find the the closest path from LeBron to Lance
or the White Mamba to the Black Mamba
Sports and Math retweeted
The perception of his value has changed,
but he's been a top 150 player (Low-End Starter) since 2015.
He's been between 100 and 150 the past 4 seasons
Sports and Math retweeted
Here it is:
All 508 since 1977 who've been a Top 60 Player in the League (produced at least 15% of the avg team's production per game)
There's a ton of metrics like Absolute, 1 Yr, 3 Yr, 5 Yr, 10 Yr, 15 Yr, 20 Yr Peaks, Career PTS Added, Production, etc.
docs.google.com/spreadsheets…
Sports and Math retweeted
NYK (3) vs IND (4) detailed Playoff Series Odds:
- Knicks are 56% favs to win series
- Home team favored in every game (NYK: ~59%, IND: ~56%)
- 50/50 if tied 1-1
Most Likely Outcomes:
1. Knicks in 7 (20%)
2. Pacers in 6 (16%)
3. Knicks in 5 (16%)
4. Pacers in 7 (14%)
Sports and Math retweeted
Top Game 1 Performances:
1. JDub
2. Jimmy
3. Jaden
4. KAT
5. Siakam
6. White
7. Turner
8. Curry
9. iHart
10. Jokic
11. Chet
12. Naz
13. Donovan
14. Harden
15. Jerome
16. Garland
17. Paolo
18. Pritchard
19. Nembhard
20. Podz
21. Dort
22. Ant
23. OG
24. Aaron W.
25. Luka
26. Shai
