Bill Radjewski (aka BlueSCar) | Software guy who dabbles in analytics | https://xitter.cf/t.co/O5HOuJVFO6

Cincinnati, OH
Joined August 2019
Bill Radjewski | CollegeFootballData.com retweeted
Here's a focused look at just Wins Above Bubble, the "most deserving" part of the playoff debate. Seems to do a nice job of sorting teams despite different W-L records and conference affiliations. HT @PDawg206 @CFB_Data
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Bill Radjewski | CollegeFootballData.com retweeted
I deleted my initial comment because I see there are different interpretations here. It states that the in-stadium game clock is the official clock. It says again later that the game clock is the in-stadium clock. No sides to be taken here. In a replay review, the official's decision will be based on the embedded game clock contained within the program feed. So, the expectation is that the broadcaster embeds the official game clock in that program feed. If not, then the official uses the clock graphic in the broadcast feed. Further, it says at the bottom that the official should only restore time when the game clock shows time should have remained, and again clarifies that the game clock is the in-stadium clock. So, with Michigan, we know the in-stadium clock showed time remaining; that's why time was added back on. The issue appears to be that the Big Ten used their own camera view of the correct clock. Presumably because NBC didn't provide it, and their broadcast clock was wrong? The team that was almost screwed here was Michigan because NBC's graphic didn't match the official clock. Everyone is going to interpret this through their own bias, but if this clarifies that the in-stadium clock is the correct time, and the burden is on the network to provide that to the officials, then I want to hear from NBC that they are going to make sure the officials have a view of the actual game clock, not their graphic, which was proven to be wrong in this game.
A memo, obtained by The Athletic, from the NCAA's secretary-rules editor and expected to go out to conferences and officials, clarifies the protocol to be used "When a replay review requires time to be restored ..." in a game.
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Bill Radjewski | CollegeFootballData.com retweeted
After most of week one in CFB, here are the top 15 RBs in terms of gaining yards on their own as opposed to yards the OL got them. Moulton led the offensive explosion at Iowa. Baugh was tremendous for HC Sumrall/OC Faulkner's debut at Florida.
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Bill Radjewski | CollegeFootballData.com retweeted
New Chart 📊 Run The Damn Ball Success Rate x Explosiveness Top right = Efficient & Explosive Week 1 through Saturday Jamal Rule, Kamari Moulton, Jadan Baugh (among others) with standout performances.
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Bill Radjewski | CollegeFootballData.com retweeted
Live scores now on the website - click the flashing "Go Live" button. Auto-updates every 30 seconds. Pick and choose which games you want to see on the scoreboard. Sort by "Game Quality". pearatings.com/cfb/fbs/games
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Bill Radjewski | CollegeFootballData.com retweeted
I wrote a little bit about how I compile data in my possession efficiency ratings work, and how I rely on a variety of sources and friends (from @CFB_Data to @SickosCommittee, if necessary) to build a data set and clean it up: bcftoys.com/whiteboard/sourc…
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Bill Radjewski | CollegeFootballData.com retweeted
Fun contest to put your model (or just prediction skills) to the test! Look at that loser in 3rd last year
Replying to @CFB_Data
Enter picks through the site, upload a CSV, or use the new Picks API. After results post, dig into the leaderboard, head-to-head matchups, and crowd wisdom. It's free and built for modelers who want a real weekly benchmark.
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Bill Radjewski | CollegeFootballData.com retweeted
Reminder @CFB_Data Bill is the goat and this is such a fun contest Hoping to do better than 5 this year, but still proud of going 1st and 5th in my first two years doing projections. My new site is going to make improvements to the model much easier than before
Replying to @CFB_Data
Enter picks through the site, upload a CSV, or use the new Picks API. After results post, dig into the leaderboard, head-to-head matchups, and crowd wisdom. It's free and built for modelers who want a real weekly benchmark.
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Bill Radjewski | CollegeFootballData.com retweeted
all-takes.com/power-ratings I'm really happy with the progress I've made this off-season in developing power ratings for college football. They are a lot of ratings out there, but these are unique, transparent, and accurate.
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Bill Radjewski | CollegeFootballData.com retweeted
Pulled Elo ratings from @CFB_Data for Monte Carlo simulation of the 2026 #Huskers football season. Ran 100,000 simulations adjusting for regression to the mean and home field advantage. Most likely outcome being 6 wins, and a bowl eligibility (6+ wins) probability of 62.36%
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Bill Radjewski | CollegeFootballData.com retweeted
Replying to @CFB_Data
Starter pack is great. Also recently made this with the API (which is very usable and well documented)
🏈 Roster continuity vs. win percentage in CFB 2025 🏈
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Bill Radjewski | CollegeFootballData.com retweeted
🏀 Where do March Madness upsets happen most often? Historically: the Elite Eight. This chart comes from the datasets in the March Madness Pack. Clean tournament data + Jupyter notebooks for bracket modeling. $25 🔗 buff.ly/7stmFQL
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Bill Radjewski | CollegeFootballData.com retweeted
🚨 New contest alert 🚨 The College Basketball Computer Model Pick’em is live. If you’ve enjoyed the CFB version, this is the next level, built specifically for college hoops modelers. 🏀📊
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Bill Radjewski | CollegeFootballData.com retweeted
📊Opponent Adjusted EPA/play Weeks 1-11 ▫️Lots of good OFF in CFP contention (Top 12 rounded out by USF + JMU) ▫️Wake + Vandy would contend for a title ▫️Worst Conf Teams: OKST, ORST, BC, MSU, UF
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Bill Radjewski | CollegeFootballData.com retweeted
was happy to help contribute a new R package to pull in college basketball data from CBBD! Please reach out or open a GitHub issue if you run into any issues using the package!
Replying to @CFB_Data
Official R support for CBBD is new for this year! Special thanks to @John_B_Edwards for driving this. R docs: buff.ly/kpwCvbq
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Bill Radjewski | CollegeFootballData.com retweeted
We crunched 368 CFB coaches (2015-2024) to settle the debate—Offense or Defense background for head coaching success? Takeaway: Offensive guys had a net of 0.8 more expected wins and a higher WOE% than Defensive experienced head coaches. WOE: Wins over Expected Expected Wins: calculated by returning production, talent rank, strength of schedule (1) Absolute WOE (2) WOE% (3) W/L% - Top 25 (4) Top 15 and Bottom 15 Data: Wiki, @CFB_Data 🏈📈 #CollegeFootball #CFB #CoachingTree
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Bill Radjewski | CollegeFootballData.com retweeted
If you model, you should join!
Interesting note from the CFBD Model Pick’em: Players making 30+ picks in Week 5: 2021 – 18 2022 – 36 2023 – 39 2024 – 37 2025 – 65 (!) That’s a MAASSIVE jump. Competition is heating up fast. 🔥
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Bill Radjewski | CollegeFootballData.com retweeted
📊 New perk for Tier 3+ patrons: Weekly Model Training Data drops are now live! Updated every Sunday night (no later than Monday) Same format as the Model Training Pack Includes opening Vegas lines (updated lines available via the CFBD API) 🔗 buff.ly/mQLES23
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Bill Radjewski | CollegeFootballData.com retweeted
Hi folks, for @CFB_Data I wrote up how to submit your predictions to the Model Pick'Em Contest using HTTP requests! Everything you need to build out a prediction submission pipeline should be in here: blog.collegefootballdata.com…
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Bill Radjewski | CollegeFootballData.com retweeted
🧠📊 Think you know college football? These 10 charts will change the way you watch the game. Field position. Success rate. Trench play. Rushing identity. And more. ➤ See the full breakdown: 🔗 buff.ly/R5WqiVn #CFB #Analytics #CollegeFootball #DataViz #CFBData
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