It was a weird unwritten rule, Rivera is the only, and I assumed once he broke the boundary others would follow. Pujols should definitely be when his time comes. What I always wondered, was how did they know that too many wouldn’t vote first ballot, to eliminate a strong viable candidate.
Posey & Molina are both HoF, are they unanimous 1st ballot? Even if not, will they get rid of artificial non-votes
Seaver was the highest % for 25 years, after the initial placement.
Griffey passed him in 2016
Jeter was one vote short after Rivera
I find it interesting that ARod is around 37% with little movement
Clemens & Bonds were around 65%.
Belief they had a great non-steroidal career?
ARod actually failed a test post regulation?
Manny is somewhat lower
Not that either deserve it, but surprised Pedroia got more votes than Wright
I was surprised he was first ballot. I suspect there will be a shift in who is a considered a hall of famer going forward. And as much of a douche Schilling is it’s really hard to make any argument baseballwise for Sabathia in the Hall ahead of him.
Pedrioa and Wright are similar to Mattingly. Good peak but big drop off after injuries. I’d put Wright ahead of Pedrioa for sure. Pedrioa probably gets bonus points from being 4" 8", ROY, MVP and 2 WS titles. I think all three should be members of the hall of very good. Not sure any of the 3 should be hall of fame inductees.
agree
Looking at next year Beltran & Jones are the only ones who look to have a chance from this year’s group. Utley would need a big jump
And I don’t think there are any real first year standouts on next year’s ballot. I’d be surprised to see a 1st ballot inductee next year.
If you look at starting pitchers today, then barring a rule change, there is a non-zero chance that no pitcher ever gets close to CCs wins total. 250 or maybe even 200 wins are the new 300.
Perhaps “wins” need to be replaced with a stat that is relevant.
Gerrit Cole is 33 and at 153 wins. I’d say he has a decent but not great chance at reaching 250.
But if you look at the active wins leader age 29 or younger it is German Marquez with 65 wins. The way starting pitchers no longer accumulate wins you are right that 250 is going to unlikely for most pitchers coming up today. You’d probably need the pitching equivalent of a guy like Juan Soto on the hitting side who came up at age 19 and was immediately elite.
And DTNF is right, other stats are going to start replacing wins for HOF voters and pitchers.
Wins were never really that relevant. Only perceived to be.
Issue is that journalists on average don’t understand math. It is why many chose Journalism (and many other majors, yes) as a major: the lack of rigorous required mathematics.
So instead they use WAR and QBR in the NFL which I have never met anyone who could coherently tell me how to calculate them and why the maths make sense
agree. schilling has all the stuff (and more) that CC has on the field.
the harold baines wing of the hall should be very crowded eventually.
Allegations of sexual assault coverup against Mariano and his wife. They are not accused of the sexual assault, but are accused of pressuring the girl to not disclose it.
Do people need to know how to calculate them or do they just need to know that the output strongly tracks with performance?
People - no
as an actuary, I would like to know
I was interested as well and chat GPT gave a pretty good summary
ESPN’s Expected Points Added (EPA) model uses a statistical approach based on regression analysis to predict the expected number of points a team will score at any given moment of the game, based on the specific situation. The model is designed to account for a variety of factors that affect the outcome of a play, such as down, distance, yardage, score differential, time left in the game, and other contextual factors.
Here’s an overview of how the EPA model works:
The core idea behind Expected Points (EP) is to evaluate the probability of scoring points from any given play. Essentially, for each situation in the game (e.g., first-and-10 from the 30-yard line), the model estimates how many points a team is expected to score based on historical data from past games. This estimate is updated as the game progresses.
The model uses historical play-by-play data to calculate how many points a team typically scores from a certain down-and-distance situation in a game. The more situations that are accounted for, the more accurately the model can predict expected points for each play.
ESPN’s EPA model typically relies on logistic regression or similar statistical regression techniques. These regression models are used to estimate the probability of a scoring event (touchdown, field goal, etc.) given various situational inputs. By analyzing a vast number of play-by-play situations, the model can determine how much each factor (such as down, yardage, and score) contributes to the likelihood of scoring on the next play.
The EPA model takes a wide range of factors into account, including:
Each play in the game adds or subtracts from the team’s EPA based on how much it improves or reduces the expected outcome of that possession. Positive EPA means the play increased the team’s chances of scoring, while negative EPA means the play decreased the likelihood of scoring.
The model is sensitive to the context of the game. If a quarterback is playing from behind, their plays will be weighted differently (more heavily, since they’re often attempting to score quickly). Similarly, plays made in clutch moments (e.g., 4th quarter, down by a touchdown) may be more impactful to the game’s outcome than a similar play earlier in the game.
In ESPN’s QBR, EPA is a central component of the model. QBR doesn’t just look at raw statistics (like passing yards or touchdowns); it factors in how much a quarterback’s actions actually contributed to the team’s ability to score. A quarterback who consistently adds positive EPA—particularly in high-leverage situations—will receive a higher QBR score.
In essence, ESPN’s EPA model is designed to quantify the value of every play a quarterback (or any player) participates in, based on historical data and game context. It gives a better understanding of how much a quarterback truly influences the outcome of a game, beyond basic box score stats, by focusing on the actual expected points generated (or lost) with each play they make.
Let me know if you’d like to dive deeper into any specific aspects of how EPA is calculated or its impact on QBR!
I got questions, not sure who can answer.
Fortunately I keep my questions numbered, in case of an emergency.
I mean, it is important to the experiment that the rat does not have a map of the maze.
I was looking through the White Sox promotions to see what could draw me in. Maybe the Dick Allen bobblehead. The weirdest one is Star Wars Day featuring a Steve Stone Stormtrooper bobblehead. There’s no picture for that one.
The one good thing about the lighter crowds is it’s much easier to bring little kids. My daughter just turned 3 when we took her to her 1st White Sox game last year and we’ll add my son this year who turns 2 in March. She had a blast at the kid zone.