It’s not the over/under, but whether we cover the point spread or not. Which historically we do about 50% of the time, and we cover it with all other B1G officials between 40-60% of the time.
Official Courtney Greene Gambling Thread
- Thread starter New Pal Boiler
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That still wouldn't answer the next set of studies that would be nice. Other teams may or may not cover 40-60% of the time as well. Knowing how "Greene" did with other teams (Big & total games) with how Greene did with Purdue tells me more about Greene.
I already understand quite clearly in the OP that the numbers were not due to chance and that there is an assignable cause associated with it. What I don't know is if Greene has similar results with other teams since he is the consideration to be tested, not if other teams also cover 40-60% that doesn't tell me much if I want to test for Greene. Any ref that blows the whistle creates more potential to be over than another ref that lets the mugging go where nobody scores much. I'm not asking anyone to run the numbers, but I think those numbers not yet understood would shed more light on Greene as a ref.
I'm not sure if we're just using different terminology...
Game 1 vs Milwaukee: Spread was Purdue -25. Purdue won by 31. Purdue is 1-0 vs the spread.
Game 2 vs Austin Peay: Spread was Purdue -25 again (coincidence). Purdue won by 19. Purdue is now 1-1 vs the spread.
Game 3 vs Marquette: Spread was Purdue -7.5. Purdue won by 5. Purdue is now 1-2 vs the spread.
Slide down to the home game vs Minnesota. Spread was Purdue -19. Purdue won by 19. That is the "tie" vs the spread.
Very true. More information would be necessary. However, in just using the small sample size of Purdue games, which Purdue is 2-20-1 ATS when Greene refs, the OTHER teams combined are 20-2-1 ATS. Therefore, the not-perfect translation is that bettors should bet on the other team to cover when Greene refs a Purdue game.
yes, got games 1 through 3 and then I gather a "loss" when predicted to win is the third number in 1-17-1? For your puposes, that data is enough for Purdue and Greene, but to understand if Greene has the same improbability with other Big Teams and just other Teams, then more data is needed for Greene under those questions. My question is the under prevalent data with Greene, with other teams, and the missing pea, is just part of who he is.
FWIW, I think Refs do in fact influence games in how they perceive things even if being totally honest and you always have the flight versus fight humanistic trait in refs. Anyway, thank you for taking the time and I hope that last number is an event that no matter the spread, the event never happened.
This is what perplexes me, though. If he was smart--and I realize that's a big "if"--he would seem wise to change this up so that *Purdue* occasionally beat the spread. But I would absolutely like to know how this plays out with other teams in games where Purdue is not involved.
I get that. Purdue seems adversely affected without any other data. I guess I missed the 20-2-1 for other teams somewhere, which is indicative that Purdue is "more " affected than other teams. I thought the info was just that 40-60% of the times other teams covered which 60-40% they diden't and unaware that we already have Greene with the Big data. The second part supports the first part which in honestly quite compelling!
Our last 23 opponents (most of whom are B1G) are a combined 20-2-1 ATS with Greene officiating.
No. The 3rd number of 1 in 1-17-1 is a "tie" against the spread, like the home Minnesota game this year. The spread was Purdue -19 and we in fact won by 19. That's why I threw that into the game-by-game record.yes, got games 1 through 3 and then I gather a "loss" when predicted to win is the third number in 1-17-1?
Appreciate the baby steps with this ignorant person on betting lines!
It's a zero-sum game. Much like our opponents for real (actual wins and losses) this year are combined 1-19. If Purdue is 2-20-1 ATS, our combined opponents are 20-2-1.
As a more than casual gambler, I can tell you that 2-20-1 is an incredible "tendency". Gamblers dream of this type of thing. Chi-Boiler does this for a living, I don't. But I promise you I have taken advantage of this many times.
TJ, I don't have any other numbers on Greene in games not involving PU. But if you do the research and find that he has this kind of discrepancy with any other team, please share that!!
TJ, I don't have any other numbers on Greene in games not involving PU. But if you do the research and find that he has this kind of discrepancy with any other team, please share that!!
I found this site:
It shows fouls called for each team for each game a certain ref calls. What it doesn't show is which ref calls the fouls. This year, Green has reffed 4 Purdue games, 2 at home, 1 road, and 1 neutral. Here are some stats that may surprise you, actually. Remember that these are for the 3-man crew, not just Green.
1/22 Maryland Purdue 14 fouls, Maryland 18
12/17 Davidson Purdue 11 fouls, Davidson 21
12/10 Nebraska Purdue 17 fouls, Nebraska 23
11/11 Austin Peay Purdue 17 fouls, AP 17.
Purdue went 0-4 ATS in the 4 games.
It shows fouls called for each team for each game a certain ref calls. What it doesn't show is which ref calls the fouls. This year, Green has reffed 4 Purdue games, 2 at home, 1 road, and 1 neutral. Here are some stats that may surprise you, actually. Remember that these are for the 3-man crew, not just Green.
1/22 Maryland Purdue 14 fouls, Maryland 18
12/17 Davidson Purdue 11 fouls, Davidson 21
12/10 Nebraska Purdue 17 fouls, Nebraska 23
11/11 Austin Peay Purdue 17 fouls, AP 17.
Purdue went 0-4 ATS in the 4 games.
I don't know that I could do that very soon if any (doing tax stuff right now) and now sure how I would find old data like refs and individual lines up to the game, but even with the first set of data it was pretty compelling against GReene on Purdue and the next set of data for other Big games only makes that compelling case even stronger. Certainly I hope any game he does with Purdue in it...I hope Purdue is heavily favored!!!
I hate tax season and just had to take a break and pop in here every now and then and so I have a lot of numbers going through my head. FWIW, I always thought and have written before that I believe there is money to be made with a statistical analysis on individual refs in general, same ref in one of the 3 locations on the floor (assuming that call was only in his area to call...knowing it would not be 100% accurate) parse out how that ref does at various locations relative to the number of calls in that area compared to what ever the choice is) and any significant interaction with other refs (does Green call less fouls with such and such than with others) only on fouls would be quite interesting and worth something to coaches as well.
Everybody sees games different and refs even with rule definitions are always down to a subject interpretation and that internal bias affects the game to some degree...even with a 100% honest ref. which I think almost all are. People says it evens out, but that is more a saying than reality because human bias with eye sight and processing that situation always contains a bias...not cheating but a bias and with two teams having differences that bias is in play...and maybe in play differently during the time a ref is in certain locations...like this guy lets it go underneath, but when he rotates out on the perimeter he blows the whistle substantially more than most. I remember a high school ref that really protected the guards (he played guard in high school).
I have a tough time watching anything outside the BIG due to time constraints but did watch Alford (Nevada) against Richard Pitino (New Mexico) and to the eye test it was a bit different...less anticipation on two reach arounds not called and a bit less physical inside...maybe other things as I slept since then...
@New Pal Boiler I missed this the first time I read...struggling with usiing access to link 3 excel files to show what I want due to some funky formating in the text without manual editing
Yes, I never looked at the inverse of Purdue with the others, but thought it was all other schools in the Big, not just the ones that played Purdue...should have looked closer at the numbers. Now is the spread determined by various incremental adjustments to get half betting on one side and the other half betting on the other side which essentially would be created by gamblers or is the spread created by some statistical measure...maybe a starting point like kenpom and then adjusted by betting? Basically what creates the spread that Greene is compared?
I was able to copy and paste the data into excel. I need to play with it and see if I can find the potential refs without editing the URL. Once I can manuever around a bit I'll think about a couple of ways at looking at fouls. There is so much data today on basketball...it is amazing!
edit--
Found the pointer to officials but not ready for the requirement needed such as registering-
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Well, Courtney Green is in East Lansing tonight, so that's a positive!
Yes, to put it simply. Both of what you said. There are statistical measures that go into a number originally getting set, but what the book wants is equal action on both sides. Initially, both factor in. Movements are caused by more information (i.e. Mahomes' injury news) along with betting patterns. If 80% of the money is coming in on Kansas City, the line will move to make it more enticing to bet Cincinnati.
just edited my above info on finding the officials (previous post), but not enough time to get value out of the registration at this time. I'm about ready to get off this and prepare for war, but I've learned a lot and so thank you. So we know the 50% prob used is not correct, but we don't know how close it is to 50% (the shift per game from the initial starting point & any errors in the stats). Anecdotally, I am shocked at times how close the game finishes so close to the line as a casual observer and I get the shot clock helps keep data sample sizes more uniform, but interesting anyway. Again, thank you!
Former NBA official Tim donaghy (allegedly) told his gambling partners that it was easy to swing games 6 pts towards or away from catching the spread, coupled with the insider knowledge of how the league wanted to pay attention to certain players/fouls those folks made a lot of cash together. The Netflix documentary about his story is pretty interesting. Problem was the big mouths on him and his buds, now if someone were to try again and keep their mouths shut….
I started to look at this a couple of years ago and didn't have enough time to understand. I think I recall that the predictability for Kenpom was a simple z-score area under the curve starting at 50% and some deviation from that midpoint for the spread in points or liklihood of winning??? All I really recall was a Z-score for that reference point ? Too long ago and maybe it wasn't predictability but spread...just know the Z-scores were used
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John Higgins seemed decent during the basketball game on Sunday against Sparty. It might be good to have him at the IU game. He might contain Greene if he is there.
Did that guy get highlights? Not every day you see a 50 something dude with frosted lettuce.
DJ Carstensen was on that crew too. He has been better this year than the last couple of seasons, IMO. We shall see.....the game shapes up to be the equivalent with whacking a hornets nest about two dozen times.
Ha, yep, he flew down for the Baylor-Texas game on Monday. They said they wanted the best but the announcer was concerned about the refs traveling around so often.
It won’t have any impact on the spread…any serious capper will look at it as “noise”. 350+ teams, hundreds of referees, and an endless amount of places to start/stop your data set means that these types of things happen.
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