VBChickens

Look how different Geno Auriemma acted with Dawn Staley last year when UConn won LMFAOO Ain’t no fun when the rabbit got the gun 😂🤣

Geno Auriemma when he loses

🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣

They know you a bitch 🤷‍♀️

GAME!!!!! COCKS!!!!!

Don’t play with Dawn! 🐐 😂 #W

What are we doing here @MLB

I’m in tears he was searching for that man as soon as the shot went in 😭😭

jesus fucking christ

This is for Phoenix and goes back to 1895. Shows the 3-day average of the departure from normal. You can see this heatwave is in a league of its own.

Fire Mainieri into the sun. If he’s not gone on Monday, I need Donati gone on Tuesday. Abject embarrassment of an athletics program. Surely someone with pull has to care. Even just a little bit.

Manieri got a case for the worst hire at a major college program of all time

Ya grandpa so gone

Hol that L tati

I just hope both teams have fun

Good ole’ Iraqi Top

I could have pitched that game we need you for the DR and Japan big dawg

Focusing on a few teams of interest, the model does not like Texas, LSU, or Auburn (#2, #3, #7) in D1 baseball as of this writing. This graphic is a bit of a stray for Tenn but everyone in this group shows the same story with good pitching and below average offense. Now using Tenn to show why the projection is so low. Looking at the stats on their roster, it's hard to argue it should be much different. Their returners (blank in From column) are either low PA guys or low production guys. Only 3 additions from the portal is asking a lot from that group. Given the caliber of the program, it would be reasonable to assume some development factor beyond what the pure data shows but I want to capture pure data signal and keep the vibes out. The projection will adjust in a few weeks if the team performs better. Expanding on the uncertainty component in the model and why the team aggregation is simulation-based, see Hunter High's projection below. His 2025 stats were good (.459 wOBA) but only 15 plate appearances means we can't be very certain of that. Meanwhile, Henry Ford has very strong priors and a relatively narrower distribution for his 2026 runs above average. Overall, the Tenn projection is a function of pitching despite all the batting commentary i've listed. Their 2025 pitching was ELITE and 2026 will probably be much less so.

Long time no post since the launch of the stat dog but I been cooking. TLDR I built a college baseball model and the results are interesting. Gonna explain the model and timestamp some "preseason" takes but I will update throughout the spring. Covering the mechanics as briefly as possible: It is conceptually similar the famous MARCEL projection system. Therein, players are projected year to year based on their prior year stats * some regression to the mean (good or bad) * development factor for their age. I used actual prior year stats but specifically focused on "skill" components like K% and BB%. From those pieces, I build up an individual projection for each players' wOBA (batters) or FIP (pitchers). I project at-bat share (batters) and innings-pitched share (pitchers) so I can build team-level projections with sum(wOBA * PA share). I built a player based model very deliberately to try to capture more signal in the transfer portal era. I use real prior stats but add in-season Bayesian increments from observed performance. So the model results start with last year(s) priors and increasingly converge towards current year as data accumulates. Two major disclaimers: 1-freshman are not real people. They will be later in the season (Bayes) but they have no priors and i'm not using recruiting data. So, their priors are imputed as league average 18 yr olds. 2-I only added very basic adjustments for strength of schedule/conference so the team level aggregation is not calibrated to create meaningful cross-conference power ratings like RPI. Long time no post since the launch of the stat dog but I been cooking. TLDR I built a college baseball model and the results are interesting. Gonna explain the model and timestamp some "preseason" takes but I will update throughout the spring. Covering the mechanics as briefly as possible: It is conceptually similar the famous MARCEL projection system. Therein, players are projected year to year based on their prior year stats * some regression to the mean (good or bad) * development factor for their age. I used actual prior year stats but specifically focused on "skill" components like K% and BB%. From those pieces, I build up an individual projection for each players' wOBA (batters) or FIP (pitchers). I project at-bat share (batters) and innings-pitched share (pitchers) so I can build team-level projections with sum(wOBA * PA share). I built a player based model very deliberately to try to capture more signal in the transfer portal era. I use real prior stats but add in-season Bayesian increments from observed performance. So the model results start with last year(s) priors and increasingly converge towards current year as data accumulates. Two major disclaimers: 1-freshman are not real people. They will be later in the season (Bayes) but they have no priors and i'm not using recruiting data. So, their priors are imputed as league average 18 yr olds. 2-I only added very basic adjustments for strength of schedule/conference so the team level aggregation is not calibrated to create meaningful cross-conference power ratings like RPI. Example distribution for K% (narrower with more samples, moving towards in-season results) To generate an actual projection from these distributions, I ran Monte Carlo simulations with 10,000 samples per team. Since each player's projected wOBA/FIP is uncertain, the end result captures expected performance per team but also how certain we should be about that result. Illustrative: So, results. In college baseball, the P4 has been overthrown by.... the Big East!!!! S/o Creighton and Seton Hall. Breaking the conference results down further by team, it is clear that the sports runs through... the pacific northwest.