@TC4THREE
I know you are a diehard Boiler fan.. what's the consensus on your boards on your potential to New Orleans and beyond??
Absolutely Scary Stuff-Purdue and refs
- Thread starter NewJerseyHawk
- Start date
@TC4THREE
I know you are a diehard Boiler fan.. what's the consensus on your boards on your potential to New Orleans and beyond??
I agree that refs impact games, but not because of “bias” ie “Courtney Greene hates Purdue” or “Bo Boroski hates Rutgers” … it’s rather their style of reffing, which they don’t change depending on the matchup (can argue if that’s good or bad, separate issue)… Boroski calls tons of fouls, and especially when there’s acting/embellishment going on … this really hurts RU, because 1) we are a physical team, and 2) Pikiell is all class and doesn’t teach our guys to flail about and flop like other teams seem to do to us all the time (especially Iowa).
like someone said in a reply tweet, Greene doesn’t call a lot of fouls inside, which hurts Purdue which is a team that has Big men that they dish to and use often, and allows underdogs to keep it closer
like someone said in a reply tweet, Greene doesn’t call a lot of fouls inside, which hurts Purdue which is a team that has Big men that they dish to and use often, and allows underdogs to keep it closer
I think it’s less about favoring teams and more about the spreads
I’m not saying this is what happened, but let’s say Purdue is -22.5 to enter a game. The official can very easily call a couple of ticky tacky fouls early in the game which would impact the rest of the game.
Imagine Edey getting called for two quick ones, sure Williams comes off the bench, but it changes the entire dynamic of the game and the spread.
Boom dog +22.5 cashes.
Again I’m not saying that’s what is happening, but it’s not like this hasn’t happened in basketball before. The sport already has a history of it, and it is 110% the easiest sport to fix if needed.
Keep in mind sports betting has grown and expanded to a ton of states now. It’s become easier for something like this to happen, there much greater access to it.
Also the talk in Vegas this season and on betting Twitter has been all about the officiating. It’s been atrocious to the point where people are questioning it. Obviously there are the sore loser types, but I watched just about every single game the first two rounds and it has been noticeably uncharacteristically bad this year IMO.
2017-18, 13-18, 2-1(15-19), ATS 15-15
CCNY(W) Perone, Snedden, Palacz
CCSU(W) Szelc, Cruz, O'Brien
ClevSt(W) Scirotto, Eppley, Kueneman, -13 W
CoppSt(W) Garrison, O'Connell, Green
Bryant(W) Steratore, Beaver, Young
ECU(W) Eades, McJunkins, Siville, -12.5 W
FSU(L) Boroski, Scirotto, Garrison, +4.5 L
@ Minn(L) Szelc, McJunkins, Dorsey, +12.5 L
MSU(L) Carstensen, Teddy V, Green, +14.5 W
NJIT(W) Scirotto, Riley, Walton,
FDU(W) Ek, Eppley, Young
Fordham(W) Steratore, Driscoll, Young, -11 W
SHU(W) McJunkins, Gaffney, Walton, +9 W
SBU(L) Szelc, Green, Cruz,
Hartford(L) Pfiefer, Eades, Garrison
@ Purdue(L) Simpson, Boroski, Riley, +19 L
Wisc(W) Steratore, O'Connell, Kueneman, +3 W
@ MSU(L) Curry, Walton, Beaver, +22 W
Ohio St(L) Scirotto, Teddy V, Pfiefer, +6 L
Iowa(W) Oglesby, Eades, Garrison, +1 W
@ Mich(L) Carstensen, McJunkins, Kueneman, +12 L
Nebraska(L) Simpson, Ek, Green, -2.5 L
@ Penn St(L) Wymer, Oglesby, Wells, +9.5 L
@ Illinois(L) Pfiefer, Curry, Carstensen, +6.5 L
Purdue(L) Boroski, Walton, Green, +15.5 W
Indiana(L) Scirotto, Teddy V, Kueneman, +2 L
@ Nebraska(L) Boroski, Oglesby, McJunkins, +9 L
Northwestern(W) Szelc, Carstensen, Ek, +3 W
@ Maryland(L) Simpson, Wymer, Beaver, +11.5 W
@ Ohio St(L) Oglesby, Eades, Garrison, +14.5 L
Illinois(L) Wymer, Teddy V, Pfiefer, +2 L
(N) Minn(W) Steratore, Szelc, Green, +1 W
(N) Indiana(W) Boroski, Riley, Ek, +7.5 W
(N) Purdue(L) Wymer, Szelc, Green, +15.5 W
2018-19, 14-16, 0-1(14-17), ATS 17-14
FDU(W) Carstensen, Beaver, Young, -12 W
Drexel(W) Simpson, Riley, McCarthy, -13.5 W
SJU(L) Chiazza, Walton, Breeding, +3 L
EMU(W) Pfiefer, Scirotto, Boroski, -7 W
BostonU(W) McCarthy, Cruz, Dorsey, -13 L
@ MiaFl(W) Roberts, Dorsey, Groover, +10.5 W
Mich St(L} Garrison, Green, Scirotto, +8 L
@ Wisconsin(L) Curry, Beaver, Oglesby, +12 W
@ Fordham(L) Potter, Tyburski, Jones, -8 L
@ SHU(L) McJunkins, Szelc, Driscoll, +6.5 W
Columbia(W) O'Brien, Floyd, McCarthy, -11.5 L
Maine(W) Phillips, Tyburski, Scirotto, -21 L
Maryland(L) Garrison, Riley, Eades, +3.5 L
Ohio St(W) Scirotto, Kueneman, Ek, +5 W
@ Minn(L) Young, Green, Beaver, +9 L
@ Purdue(L) Cruz, Simpson, Boroski, +14.5 L
Northwestern(L) McCarthy, Walton, Ek, +1.5 L
Nebraska(W) Kueneman, Garrison, Szelc, +8.5 W
@ Penn St(W) Carstensen, Green, Dorsey, +7 W
Indiana(W) Simpson, Wymer, Boroski, +1.5 W
@ Ohio St(L) Riley, Green, Beaver, +10.5 L
Michigan(L) Cruz, Scirotto, Oglesby, +9.5 L
@ Illinois(L) Kimble, Beaver, Carstensen, +5.5 W
@ Northwestern(W), *no refs listed*, +5.5 W
Iowa(L) Curry, Kueneman, Carstensen, +3.5 W
@ Mich St(L) Kimble, Pfiefer, Ek, +15.5 W
Minnesota(W) Scirotto, Wymer, Boroski, -1.5 W
@ Iowa(W), Walton, Kueneman, Simpson, +8.5 W
Penn St(L), Boroski, Pfiefer, Green, +1.5 W
@ Indiana(L) Walton, Ek, Kimble, +7.5 L
(N) Nebraska(L) Pfiefer, Szelc, Scirotto, -1.5 L
2019-20 20-11, ATS 19-10-1
Bryant(W) Whetstone, Scirotto, Boroski, -22 L
Niagara(W) Beaver, Riley, Carstensen, -21.5 W
Drexel(W), Pfiefer, Szelc, Oglesby, -14.5 L
(N) St Bonaventure(L) Tyburski, Eppley, Scirotto, -9 L
SF Austin(W) Curry, Oglesby, Boroski, -11.5 W
NJIT(W) Dorsey, Eppley, Szelc, -13 W
UMass(W) McCarthy, Green, Dorsey, -9 W
@ Pitt(L) Cassell, Covington, Luckie, +2.5 L
@ Mich St(L) Green, Pfiefer, Oglesby, +14.5 W
Wisconsin(W) Curry, Scirotto, Wymer, 0 W
SHU(W) McJunkins, Dorsey, Boroski, +1.5 W
Lafayette(W) Daily, Cruz, Riley, -15.5 W
Caldwell(W) Wells, O'Brien, Young,
@ Nebraska(W) Garrison, Pfiefer, Scirotto, -4.5 W
Penn St(W) Cruz, Curry, Szelc, +1.5 W
@ Illinois(L) Beaver, Wymer, Oglesby, +5 W
Indiana(W) Pfiefer, Carstensen, Boroski, -4 W
Minesota(W) Beaver, Szelc, Oglesby, -5.5 W
@ Iowa(L) Walton, Daily, Green, +5 P
Nebraska(W) McCarthy, Young, Carstensen, -13.5 L
Purdue(W) Pfiefer, Beaver, Oglesby, -3 W
(N) Michigan(L) Ek, Cruz, Eppley, +1.5 L
@ Maryland(L) Green, Kueneman, Scirotto, +7.5 W
Northwestern(W) Curry, McJunkins, Garrison, -10.5 L
@ Ohio St(L) Wymer, Carstensen, Eades, +5.5 L
Illinois(W) Beaver, Pfiefer, Boroski, -4.5 W
Michigan(L) Daily, Cruz, Scirotto, -4 L
@ Wisconsin(L) Green, Wymer, Oglesby, +5.5 L
@ Penn St(L) Garrison, Scirotto, Boroski, +4.5 W
Maryland(W) Eppley, O'Oconnell, Szelc, -1 W
@ Purdue(W) Green, Oglesby, Wymer, +5.5 W
(N) Mich(canc) would have loved to know
2020-21 14-10, 1-1, 1-1(16-12), ATS 15-12-1
Sacred Heart(W) Marabito, Whetstone, Cruz, -27 L
Fairleigh Dickinson(W) Kitts, McJunkins, Ek, -25 L
Hofstra(W) Young, Kueneman, Dorsey, -12 P
Syracuse(W) Young, Pfiefer, Szelc, -5 W
@ Maryland(W) Boroski, Beaver, Carstensen, +2.5 W
Illinois (W) Riley, Pfiefer, Scirotto, +1.5 W
@ Ohio St(L) Young, Szelc, Boroski, +3 L
Purdue(W) Kueneman, Carstensen, Scirotto, -1.5 W
Iowa (L) McJunkins, Pfiefer, Boroski, +3.5 W
@ Mich St(L) Green, Szelc, Scirotto, +3 L
Ohio St (L) Garrison, Eppley, Carstensen, -3.5 L
Wisconsin (L) Kueneman, Green, Carstensen, +2.5 L
@ Penn St(L) Riley, Dorsey, Carstensen, -2.5 L
@ Indiana(W) McJunkins, Higgins, Eppley, +5.5 W
Michigan St(W) Szelc, Carstensen, Boroski, -4 W
@ Northwestern(W) Garrison, Green, Riley, -3.5 W
Minnesota(W) Young, Eppley, Szelc, -5.5 L
@ Iowa(L) Wells, Carstensen, Scirotto, +7 L
Northwestern(W) Cruz, Keuneman, Szelc, -8 W
@ Michigan(L) McJunkins, Ek, Scirotto, +9 W
Maryland(L) Garrison, Riley, Szelc, -4.5 L
Indiana(W) Carstensen, Green, Boroski, W -3.5
@ Nebraska(L) Dorsey, Higgins, Kueneman, -8 L
@ Minnesota(W) Wells, Green, Carstensen, -3.5 L
(N) Indiana(W) Garrison, Green, Szelc, -2.5 W
(N) Illinois(L) McJunkins, Pfiefer, Szelc, +8 L
(N) Clemson(W) Hartness, Pettigrew, Wymer, -2.5 L
(N) Houston(L) Brill, Green, Daily, +7.5 W
2021-22, 18-12, 0-1, 0-1(18-14), ATS 14-17-1
Lehigh(W) Wells, Kimble, Cruz
Merrimack(W) Rahir, Kashirsky, Scirotto
NJIT(W) Kueneman, Beaver, Eppley
@ Depaul(L) Garrison, Riley, Szelc
Lafayette(L) Honacki, McNutt, Boroski
@ UMass(L) Smith, Young, Walton
Clemson(W) Ek, Garrison, Pfiefer
@ Illinois(L) Beaver, Szelc, Boroski
Purdue(W) Daily, Green, Carstensen
@ Seton Hall(L) Boroski, Ayers, Anderson
Maine(W) Corbett, Kitts, Riley
CCSU(W) Whetstone, Young, Scirotto
Michigan(W) Kueneman, Oglesby, Scirotto
Nebraska(W) Cruz, Pfiefer, Green
@ Penn St(L) Garrison, Beaver, Eppley
@ Maryland(W) Ek, Scirotto, Carstensen
Iowa(W) Wells, Eppley, Green
@ Minnesota(L) McNutt, Cruz, Pfiefer
Maryland(L) McJunkins, Daily, Carstensen
@ Nebraska(W) Wells, Cruz, Kueneman
@ Northwestern(L) Simpson, Green, Boroski
Mich St(W) Carstensen, Pfiefer, Szelc
Ohio St(W) Garrison, Simpson, Boroski
@ Wisconsin(W) McJunkins, Curry, Beaver
Illinois(W) Wells, Ek, Szelc
@ Purdue(L) Ek, Smith, Kimble
@ Michigan(L) Young, Garrison, Carstensen
Wisconsin(L) Daily, Green, Boroski
@ Indiana(W) Riley, Kimble, Pfiefer
Penn St(W) Walton, Garrison, Szelc
(N) Iowa(L) Cruz, Pfiefer, Boroski
(N) Notre Dame(L) Rorke, Green, Driscoll
I don't think it is the refs doing it intentionally, but each ref has their own way of calling fouls and such. You have too many tight refs it can help one team and hurts another, if you have too many loose refs it can hurt one team and help another.
There is no fix to it.
Maybe others can do the ATS and records by ref stuff
Side note, we were undefeated in 2017-18 with Steratore as lead ref before he retired. I think Boroski and a few others style favors Purdue over Indiana
CCNY(W) Perone, Snedden, Palacz
CCSU(W) Szelc, Cruz, O'Brien
ClevSt(W) Scirotto, Eppley, Kueneman, -13 W
CoppSt(W) Garrison, O'Connell, Green
Bryant(W) Steratore, Beaver, Young
ECU(W) Eades, McJunkins, Siville, -12.5 W
FSU(L) Boroski, Scirotto, Garrison, +4.5 L
@ Minn(L) Szelc, McJunkins, Dorsey, +12.5 L
MSU(L) Carstensen, Teddy V, Green, +14.5 W
NJIT(W) Scirotto, Riley, Walton,
FDU(W) Ek, Eppley, Young
Fordham(W) Steratore, Driscoll, Young, -11 W
SHU(W) McJunkins, Gaffney, Walton, +9 W
SBU(L) Szelc, Green, Cruz,
Hartford(L) Pfiefer, Eades, Garrison
@ Purdue(L) Simpson, Boroski, Riley, +19 L
Wisc(W) Steratore, O'Connell, Kueneman, +3 W
@ MSU(L) Curry, Walton, Beaver, +22 W
Ohio St(L) Scirotto, Teddy V, Pfiefer, +6 L
Iowa(W) Oglesby, Eades, Garrison, +1 W
@ Mich(L) Carstensen, McJunkins, Kueneman, +12 L
Nebraska(L) Simpson, Ek, Green, -2.5 L
@ Penn St(L) Wymer, Oglesby, Wells, +9.5 L
@ Illinois(L) Pfiefer, Curry, Carstensen, +6.5 L
Purdue(L) Boroski, Walton, Green, +15.5 W
Indiana(L) Scirotto, Teddy V, Kueneman, +2 L
@ Nebraska(L) Boroski, Oglesby, McJunkins, +9 L
Northwestern(W) Szelc, Carstensen, Ek, +3 W
@ Maryland(L) Simpson, Wymer, Beaver, +11.5 W
@ Ohio St(L) Oglesby, Eades, Garrison, +14.5 L
Illinois(L) Wymer, Teddy V, Pfiefer, +2 L
(N) Minn(W) Steratore, Szelc, Green, +1 W
(N) Indiana(W) Boroski, Riley, Ek, +7.5 W
(N) Purdue(L) Wymer, Szelc, Green, +15.5 W
2018-19, 14-16, 0-1(14-17), ATS 17-14
FDU(W) Carstensen, Beaver, Young, -12 W
Drexel(W) Simpson, Riley, McCarthy, -13.5 W
SJU(L) Chiazza, Walton, Breeding, +3 L
EMU(W) Pfiefer, Scirotto, Boroski, -7 W
BostonU(W) McCarthy, Cruz, Dorsey, -13 L
@ MiaFl(W) Roberts, Dorsey, Groover, +10.5 W
Mich St(L} Garrison, Green, Scirotto, +8 L
@ Wisconsin(L) Curry, Beaver, Oglesby, +12 W
@ Fordham(L) Potter, Tyburski, Jones, -8 L
@ SHU(L) McJunkins, Szelc, Driscoll, +6.5 W
Columbia(W) O'Brien, Floyd, McCarthy, -11.5 L
Maine(W) Phillips, Tyburski, Scirotto, -21 L
Maryland(L) Garrison, Riley, Eades, +3.5 L
Ohio St(W) Scirotto, Kueneman, Ek, +5 W
@ Minn(L) Young, Green, Beaver, +9 L
@ Purdue(L) Cruz, Simpson, Boroski, +14.5 L
Northwestern(L) McCarthy, Walton, Ek, +1.5 L
Nebraska(W) Kueneman, Garrison, Szelc, +8.5 W
@ Penn St(W) Carstensen, Green, Dorsey, +7 W
Indiana(W) Simpson, Wymer, Boroski, +1.5 W
@ Ohio St(L) Riley, Green, Beaver, +10.5 L
Michigan(L) Cruz, Scirotto, Oglesby, +9.5 L
@ Illinois(L) Kimble, Beaver, Carstensen, +5.5 W
@ Northwestern(W), *no refs listed*, +5.5 W
Iowa(L) Curry, Kueneman, Carstensen, +3.5 W
@ Mich St(L) Kimble, Pfiefer, Ek, +15.5 W
Minnesota(W) Scirotto, Wymer, Boroski, -1.5 W
@ Iowa(W), Walton, Kueneman, Simpson, +8.5 W
Penn St(L), Boroski, Pfiefer, Green, +1.5 W
@ Indiana(L) Walton, Ek, Kimble, +7.5 L
(N) Nebraska(L) Pfiefer, Szelc, Scirotto, -1.5 L
2019-20 20-11, ATS 19-10-1
Bryant(W) Whetstone, Scirotto, Boroski, -22 L
Niagara(W) Beaver, Riley, Carstensen, -21.5 W
Drexel(W), Pfiefer, Szelc, Oglesby, -14.5 L
(N) St Bonaventure(L) Tyburski, Eppley, Scirotto, -9 L
SF Austin(W) Curry, Oglesby, Boroski, -11.5 W
NJIT(W) Dorsey, Eppley, Szelc, -13 W
UMass(W) McCarthy, Green, Dorsey, -9 W
@ Pitt(L) Cassell, Covington, Luckie, +2.5 L
@ Mich St(L) Green, Pfiefer, Oglesby, +14.5 W
Wisconsin(W) Curry, Scirotto, Wymer, 0 W
SHU(W) McJunkins, Dorsey, Boroski, +1.5 W
Lafayette(W) Daily, Cruz, Riley, -15.5 W
Caldwell(W) Wells, O'Brien, Young,
@ Nebraska(W) Garrison, Pfiefer, Scirotto, -4.5 W
Penn St(W) Cruz, Curry, Szelc, +1.5 W
@ Illinois(L) Beaver, Wymer, Oglesby, +5 W
Indiana(W) Pfiefer, Carstensen, Boroski, -4 W
Minesota(W) Beaver, Szelc, Oglesby, -5.5 W
@ Iowa(L) Walton, Daily, Green, +5 P
Nebraska(W) McCarthy, Young, Carstensen, -13.5 L
Purdue(W) Pfiefer, Beaver, Oglesby, -3 W
(N) Michigan(L) Ek, Cruz, Eppley, +1.5 L
@ Maryland(L) Green, Kueneman, Scirotto, +7.5 W
Northwestern(W) Curry, McJunkins, Garrison, -10.5 L
@ Ohio St(L) Wymer, Carstensen, Eades, +5.5 L
Illinois(W) Beaver, Pfiefer, Boroski, -4.5 W
Michigan(L) Daily, Cruz, Scirotto, -4 L
@ Wisconsin(L) Green, Wymer, Oglesby, +5.5 L
@ Penn St(L) Garrison, Scirotto, Boroski, +4.5 W
Maryland(W) Eppley, O'Oconnell, Szelc, -1 W
@ Purdue(W) Green, Oglesby, Wymer, +5.5 W
(N) Mich(canc) would have loved to know
2020-21 14-10, 1-1, 1-1(16-12), ATS 15-12-1
Sacred Heart(W) Marabito, Whetstone, Cruz, -27 L
Fairleigh Dickinson(W) Kitts, McJunkins, Ek, -25 L
Hofstra(W) Young, Kueneman, Dorsey, -12 P
Syracuse(W) Young, Pfiefer, Szelc, -5 W
@ Maryland(W) Boroski, Beaver, Carstensen, +2.5 W
Illinois (W) Riley, Pfiefer, Scirotto, +1.5 W
@ Ohio St(L) Young, Szelc, Boroski, +3 L
Purdue(W) Kueneman, Carstensen, Scirotto, -1.5 W
Iowa (L) McJunkins, Pfiefer, Boroski, +3.5 W
@ Mich St(L) Green, Szelc, Scirotto, +3 L
Ohio St (L) Garrison, Eppley, Carstensen, -3.5 L
Wisconsin (L) Kueneman, Green, Carstensen, +2.5 L
@ Penn St(L) Riley, Dorsey, Carstensen, -2.5 L
@ Indiana(W) McJunkins, Higgins, Eppley, +5.5 W
Michigan St(W) Szelc, Carstensen, Boroski, -4 W
@ Northwestern(W) Garrison, Green, Riley, -3.5 W
Minnesota(W) Young, Eppley, Szelc, -5.5 L
@ Iowa(L) Wells, Carstensen, Scirotto, +7 L
Northwestern(W) Cruz, Keuneman, Szelc, -8 W
@ Michigan(L) McJunkins, Ek, Scirotto, +9 W
Maryland(L) Garrison, Riley, Szelc, -4.5 L
Indiana(W) Carstensen, Green, Boroski, W -3.5
@ Nebraska(L) Dorsey, Higgins, Kueneman, -8 L
@ Minnesota(W) Wells, Green, Carstensen, -3.5 L
(N) Indiana(W) Garrison, Green, Szelc, -2.5 W
(N) Illinois(L) McJunkins, Pfiefer, Szelc, +8 L
(N) Clemson(W) Hartness, Pettigrew, Wymer, -2.5 L
(N) Houston(L) Brill, Green, Daily, +7.5 W
2021-22, 18-12, 0-1, 0-1(18-14), ATS 14-17-1
Lehigh(W) Wells, Kimble, Cruz
Merrimack(W) Rahir, Kashirsky, Scirotto
NJIT(W) Kueneman, Beaver, Eppley
@ Depaul(L) Garrison, Riley, Szelc
Lafayette(L) Honacki, McNutt, Boroski
@ UMass(L) Smith, Young, Walton
Clemson(W) Ek, Garrison, Pfiefer
@ Illinois(L) Beaver, Szelc, Boroski
Purdue(W) Daily, Green, Carstensen
@ Seton Hall(L) Boroski, Ayers, Anderson
Maine(W) Corbett, Kitts, Riley
CCSU(W) Whetstone, Young, Scirotto
Michigan(W) Kueneman, Oglesby, Scirotto
Nebraska(W) Cruz, Pfiefer, Green
@ Penn St(L) Garrison, Beaver, Eppley
@ Maryland(W) Ek, Scirotto, Carstensen
Iowa(W) Wells, Eppley, Green
@ Minnesota(L) McNutt, Cruz, Pfiefer
Maryland(L) McJunkins, Daily, Carstensen
@ Nebraska(W) Wells, Cruz, Kueneman
@ Northwestern(L) Simpson, Green, Boroski
Mich St(W) Carstensen, Pfiefer, Szelc
Ohio St(W) Garrison, Simpson, Boroski
@ Wisconsin(W) McJunkins, Curry, Beaver
Illinois(W) Wells, Ek, Szelc
@ Purdue(L) Ek, Smith, Kimble
@ Michigan(L) Young, Garrison, Carstensen
Wisconsin(L) Daily, Green, Boroski
@ Indiana(W) Riley, Kimble, Pfiefer
Penn St(W) Walton, Garrison, Szelc
(N) Iowa(L) Cruz, Pfiefer, Boroski
(N) Notre Dame(L) Rorke, Green, Driscoll
I don't think it is the refs doing it intentionally, but each ref has their own way of calling fouls and such. You have too many tight refs it can help one team and hurts another, if you have too many loose refs it can hurt one team and help another.
There is no fix to it.
Maybe others can do the ATS and records by ref stuff
Side note, we were undefeated in 2017-18 with Steratore as lead ref before he retired. I think Boroski and a few others style favors Purdue over Indiana
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I had a similar thought. Rather than looking at just Purdue, an analysis of records of all B1G schools would be, IMO, more relevant.
Why doesn’t Pat Driscoll (aka mr. Bean) ref at the RAC any more
I swear every game at RAC we get 2 of
Green
Bo
Carstensen
Pfeiffer
I swear every game at RAC we get 2 of
Green
Bo
Carstensen
Pfeiffer
Sample size isn’t the issue. You are correct that sample size is accounted for in the p-value.
The issue is that if you start looking at multiple refs the odds of finding one with a p-value that looks interesting rapidly increase. If you look at 8 refs (as this guy did) and they are completely fair there is a roughly 25% chance that one of them will have a p-value of 3.57% of lower.
The math for this is:
Odds 1 ref has a p-value > 3.57% = 1 - 3.57% = 96.43%
Odds 8 independent refs ALL have p-values > 3.57% = 0.9643^8 which roughly = 75%
So the odds you find one out of 8 with a p-value < 3.57% ~= 1-.75 = 25%
So about as interesting as flipping a coin and getting 2 heads in a row
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The Purdue fan base is one that is constantly waiting for the other shoe to drop. We've been a very good basketball team over the last 40 years and had several years where we could've gone to the Final Four or possibly even won a national championship but we've seen us fall short in a variety of ways for a variety of reasons. I think most of us believe we're the best team left in the East region but are fearful of what's going to happen this year. Since last going to the Final Four in 1980 (before I can remember) we've seen the following:
1988 - 1 seed got upset by Mitch Richmond and Kansas State in Sweet 16
1990 - 2 seed got upset in Indy by 10 seed Texas in 2nd round.
1994 - 1 seed with NPOY Glenn Robinson. Lost to Duke in E8 with an unusually bad game by Big Dog. Years later it would come back he hurt his back the night before wrestling with teammates in the hotel.
1996 - 1 seed upset by Georgia in 2nd round. This year we simply weren't very talented and got the most out of roster in the Big Ten but were exposed in the tournament.
1998 - Was #5 in the country before losing our best player to a sprained ankle on February 10th. He missed 10 games and came back but was not the same. Ended up as a 2 seed and losing to Stanford in the S16.
2000 - Advanced to E8 as a 6 seed but lost to 8 seed Wisconsin there.
2010 - #3 in the country when Robbie Hummel goes down with an ACL. Get penalized in seeding. Lose to eventual national champion Duke in Sweet 16 without him.
2018 - 2 seed and lose Isaac Haas in 2nd round game. Lose to Texas Tech in S16 without him.
2019 - 3 seed with Carsen Edwards shooting out of his mind and actually have eventual national champion Virginia all but beat but they make an insane play to send the game to OT and advance.
To a lesser extent, when we ascended to #1 this season for the first time in program history, it wasn't even a full week before we had someone hitting a half-court shot at the buzzer to knock us out of the position.
So to answer your question, I'd say the majority of the fan base is too nervous to get excited for what looks like an incredible set up for us with the bracket breaking like it has.
Taking history, paranoia, and emotion out of the equation, I think we have a team that presents a unique challenge for most others that we play. In a way, I like our matchups against good teams from other conferences that haven't had to play us over conference teams that are used to playing us and know how best to counter the size advantages we have. If we play well, which isn't a guarantee, I like our chances against our opponents this weekend. I also think we match up pretty well against Gonzaga or Duke. If we should be so fortunate to get there, I think Arizona is the one matchup that scares me the most even if we play well.
fluox...not sure your logic holds either. p value indicates the liklihood that the null hypothesis is true, which in this case is 'no ref bias'. Here, a p value is .0375, which is below the standard .05 accept/reject threshold. The null hypothesis is rejected, and there is scientifically-supported evidence of bias. It's weakly stastically significant, but still significant by accepted standards
Had he looked at 300 refs and found just 1 of them to have lopsided ATS outcomes, then the p value would be much, much lower....like .005 which much more convincing. But .0375 is still highly convincing, enough to overturn the null hypoth.
I don't get where your 25% comes into play. Nothing suggests (to me) that there's a 25% chance of a p = .0375. That's not how it works.
Your 'two heads in a row' analogy is wrong. Yes 25% chance that happens, but not the same as 1 of 8 refs with ATS outcomes so skewed.
To me, fly in the ointment is that there are multiple refs in each game, so the ability of just one of them to steer a score margin is limited.
Had he looked at 300 refs and found just 1 of them to have lopsided ATS outcomes, then the p value would be much, much lower....like .005 which much more convincing. But .0375 is still highly convincing, enough to overturn the null hypoth.
I don't get where your 25% comes into play. Nothing suggests (to me) that there's a 25% chance of a p = .0375. That's not how it works.
Your 'two heads in a row' analogy is wrong. Yes 25% chance that happens, but not the same as 1 of 8 refs with ATS outcomes so skewed.
To me, fly in the ointment is that there are multiple refs in each game, so the ability of just one of them to steer a score margin is limited.
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I did the math in my post. The p-value is for a single ref. He looked at 8 refs.
The p-value answers the question “what is the probability that specific ref X has results this extreme?”
The math I did answers the question “what is the probability that at least one out of 8 refs has results this extreme?”
The answer to question 1 is ~3.5%. The answer to question 2 is ~25%.
To extend this a bit further, this guy found 8 refs that did at least 20 Purdue games over the last 5 years. If you assume there is an average of 8 such refs for each of the 358 Division I schools, then you have 2,864 school/ref combos. You would expect to find 102 (2864 * 3.57%) school/ref pairs with results at least this extreme due to randomness alone. You would have a >50% chance (51.13% to be exact) of finding at least one ref/school pair with a p-value of <= .00025 by pure randomness alone.
Examples of how refs paired with others helps/hurts
Cruz,
6 non conference games (5-1)
17-18 CCSU 71-67 W, Szelc, O'Brien
17-18 Stony Brook 73-75 L, Green, Szelc
18-19 Boston U 54-44 W, McCarthy, Dorsey
19-20 Lafayette 63-44 W, Daily, Riley
20-21 Sacred Heart 86-63 W, Marabito, Whetstone
21-22 Lehigh 73-71 W, Wells, Kimble
B1G conference game(4-6)
18-19 @ Purdue 54-89 L, Simpson, Boroski
18-19 Michigan 65-77 L, Scirotto, Oglesby
19-20 Penn St 72-61 W, Curry, Szelc
19-20 (N) Michigan 63-69 L, Ek, Eppley
19-20 Michigan 52-60 L, Daily, Scirotto
20-21 Northwestern 64-50 W, Kueneman, Szelc
21-22 Nebraska 93-68 W, Pfiefer, Green
21-22 @ Minnesota 65-68 L, McNutt, Pfiefer
21-22 @ Nebraska 62-60 W, Wells, Kueneman
21-22 (N) Iowa 74-84 L, Pfiefer, Boroski
0-3 vs UM with Cruz, does it mean anything?
There is nothing you can really do about it.
Boroski,
17-18, 1-4 W vs *Ind, L FSU, @ Pur, Pur, Neb
18-19, 3-2, W EMU, *Ind, Minn, L @ Pur, PSU
19-20, 5-1, W Bryant, SFAustin, SHU, *Ind, Illinois, L @ PSU
20-21, 3-2, W @ Md, MSU, *Ind, L @ Ohio St, Iowa
21-22, 1-6, W Ohio St, L Laf, @ Ill, @ SHU, @ NW, Wisc, (N) Iowa
4-0 vs Ind, 0-3 vs Purdue, 0-2 vs PSU, Iowa
1-1 vs SHU, Illinois, Ohio St, Home win, Road loss
Do refs have unintentional hidden biases, maybe?
Will we be 3-2 next year if Bo refs 5 games?
Cruz,
6 non conference games (5-1)
17-18 CCSU 71-67 W, Szelc, O'Brien
17-18 Stony Brook 73-75 L, Green, Szelc
18-19 Boston U 54-44 W, McCarthy, Dorsey
19-20 Lafayette 63-44 W, Daily, Riley
20-21 Sacred Heart 86-63 W, Marabito, Whetstone
21-22 Lehigh 73-71 W, Wells, Kimble
B1G conference game(4-6)
18-19 @ Purdue 54-89 L, Simpson, Boroski
18-19 Michigan 65-77 L, Scirotto, Oglesby
19-20 Penn St 72-61 W, Curry, Szelc
19-20 (N) Michigan 63-69 L, Ek, Eppley
19-20 Michigan 52-60 L, Daily, Scirotto
20-21 Northwestern 64-50 W, Kueneman, Szelc
21-22 Nebraska 93-68 W, Pfiefer, Green
21-22 @ Minnesota 65-68 L, McNutt, Pfiefer
21-22 @ Nebraska 62-60 W, Wells, Kueneman
21-22 (N) Iowa 74-84 L, Pfiefer, Boroski
0-3 vs UM with Cruz, does it mean anything?
There is nothing you can really do about it.
Boroski,
17-18, 1-4 W vs *Ind, L FSU, @ Pur, Pur, Neb
18-19, 3-2, W EMU, *Ind, Minn, L @ Pur, PSU
19-20, 5-1, W Bryant, SFAustin, SHU, *Ind, Illinois, L @ PSU
20-21, 3-2, W @ Md, MSU, *Ind, L @ Ohio St, Iowa
21-22, 1-6, W Ohio St, L Laf, @ Ill, @ SHU, @ NW, Wisc, (N) Iowa
4-0 vs Ind, 0-3 vs Purdue, 0-2 vs PSU, Iowa
1-1 vs SHU, Illinois, Ohio St, Home win, Road loss
Do refs have unintentional hidden biases, maybe?
Will we be 3-2 next year if Bo refs 5 games?
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Not seeing it that way…but l don’t really care. What is your reasoning to multiply the p value times n ?
There were 8 refs that met the criteria…so 8 chances of finding the one “outlier).
Because there are n of them..
If someone takes 100 3 pointers and they are a 33% shooter, how many do you expect them to make?
Hint: the “p-value” of an experiment where that guy takes 1 3 pointer and makes it is 0.33
Ok, now I'm finally calling bs. I finally see what you're arguing. That has nothing to do with p value. you think p value is probability of an event. that's wrong. A shooting percentage isn't a p value.
p value represents the probability of different outcomes belonging to different populations/outcomes.
your p value isn't a comparison.
In null-hypothesis significance testing, the p-value[note 1] is the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct.
p-value - Wikipedia
Right, and that is not the probability of an event, of shooting percentage, etc. Thanks for discrediting yourself and citing exactly what I tried to explain to you.In null-hypothesis significance testing, the p-value[note 1] is the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct.
p-value - Wikipedia
en.m.wikipedia.org
Lol. You must have been a treat for your teachers in school.
This a limit to how much time I am willing to spend trying to explain concepts to someone who is for some reason actively hostile to learning and I’m afraid we have reached it.
You may find this Wikipedia page to be relevant to your situation:
https://en.m.wikipedia.org/wiki/Dunning–Kruger_effect
Good luck and have fun
Beyond the statistical analysis why would a corrupt ref show disfavor towards one team, not in wins and losses but ATS? For betting purposes? If so then wouldn't it be much less noticeable if the games were chosen at random? Or does he only like to win $$$ when Purdue doesn't cover?
In theory if the guy hated Purdue he probably can’t make them lose every game but if his calls are biased against them they would do worse when he refs than expected. This would show up in the ATS record with less noise than the actual record (because every spread should be roughly 50/50 whereas you would need to have some model for what the expected actual win percentage would be).
But that’s just in theory. In practice what the guy found isn’t remotely interesting for the reasons explained by my posts and also illustrated by the excellent xkcd comic that kcg88 posted.
True and I was reading through and didn't feel like going back to see the W-L record.
But the "scary" stuff seemed to be centered around the ATS record with the implication being that the ref is doing this for betting purposes.
Yeah not sure if that was OPs implication. If so, I agree with you, seems like it would make more sense not to bet against the same team every time lol
The null hypothesis is that he’s a 33% shooter.
But probabilities are probabilities, you can operate on probabilities that come from statistical tests the same way as any other probability. If you had any idea what you were talking about beyond being able to regurgitate the terminology you would understand that already.
But probabilities are probabilities, you can operate on probabilities that come from statistical tests the same way as any other probability. If you had any idea what you were talking about beyond being able to regurgitate the terminology you would understand that already.
There may be nothing going on in this instance. However there are different ways to make dirty money. He may not be doing the gambling - for instance he could be "influenced" by outside parties / dark money looking for a gambling or program level edge. For me its not a stretch to believe that the stench of bag men who have historically illegally funded efforts to get recruits to certain programs somehow also extends to referees. However, logic might suggest in such cases - the impact would be more identifiable towards a referee to program's statistical win trending rather than losing ATS.
While the tweeters in question will not say the refs are gambling and manipulating scores... I'll say it this way.. in this current world with so much focus on greed and so little on honor.. what are the odds that there are refs out there right now gambling and manipulating scores?
The stats as shown in the tweets are one way of finding those bastards.
The stats as shown in the tweets are one way of finding those bastards.
While interesting and able to frame this idea better, the goal was to find refs who produce strange results. So find every team and every game that suspect refs produce such results and see if it happens many times, across teams and conferences. Perhaps it is only really good teams that present such an opportunity. Purdue is a high-scoring team that results in big spreads... maybe it is easier to manipulate that.. and more rewarding.. without risking changing the outcome of the game in a win/loss scenario.
In a COIN TOSS scenario.. what if you have 10 "flippers".. and one of the flippers produces the low-probability result way too often? Does he use a loaded coin? Does he have some high skill in producing a desired flip result?
Though St Peters got away with murder tonight mauling the inside players of Purdue. Ivey looked like he was on Space Mountain in Disney World not in a Final 16 game. Looked the same last game. His stock declined over these two games as pressure seems to have gotten to him. Kudos to the Jersey City Peacocks.
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