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We have just edged past SC for #1 on Massey. I know it's early, and the only #1 that really matters is after winning the Natty, but still, it's nice be #1 


You are very brave to post this!!!We have just edged past SC for #1 on Massey. I know it's early, and the only #1 that really matters is after winning the Natty, but still, it's nice be #1![]()
SOS favors UConn at this point of the season.Surprised Massey did that off the close call.
The Massey numbers are computer generated and not at all influenced by opinions, observations, etcSurprised Massey did that off the close call.
Just above the team listings and to the left, you’ll see “Key.” Just click that.Where can I find what the column headings mean?
They are. It can change day to day whenthe two teams are this close. All it takes is a good win or a bad loss for a team that either of them has already played.I guess I don’t get it? It looks like SC is #1 on this chart.
It’s pretty much been, in the words of Dick Vitale, “Cupcake City Baby!” Other than a couple of real basketball games yesterday and UConn’s typically strong OOC schedule, a lot of top teams are playing more cupcakes than you find at a Junior HS bake sale. I think LSU set a record reeling off 6 straight games when the scored 100 pts or more playing against the Sisters of the Poor.Yesterday seemed to be "pick a cupcake to play" day for the top teams. Utah sure seemed a step down after Michigan, but then I saw that South Carolina played a school named Queens University, and rolled 121 points on those poor kids. You don't get to be number 1 after that.
It must not matter analytically in terms of ranking and/or seeding. That could be bad for the product and fan enjoyment. The kids miss out too.It’s pretty much been, in the words of Dick Vitale, “Cupcake City Baby!” Other than a couple of real basketball games yesterday and UConn’s typically strong OOC schedule, a lot of top teams are playing more cupcakes than you find at a Junior HS bake sale. I think LSU set a record reeling off 6 straight games when the scored 100 pts or more playing against the Sisters of the Poor.
Obviously, the P4 teams will pick up their competition once they start conference play. But at the risk of riling up the TN haters around here, Pat Summit’s philosophy that they would play “anyone, anywhere, anytime” was laudable, but no longer seems relevant for most teams.
UConn’s opponents Ohio St., Louisville, MI and FSU have SoS as 8, 9, 10 and 12, respectively. Others are Utah (31) and Loyola (72).Love looking at LSU's SoS.
I'm guessing (?) that the ratings still include some of last season's (tournament) games? Otherwise, wouldn't Texas' SoS be better than ours?
Massey does indeed start out with last seasons ratings and then diminish their weight. I don’t recall at what point last years value is gone.Love looking at LSU's SoS.
I'm guessing (?) that the ratings still include some of last season's (tournament) games? Otherwise, wouldn't Texas' SoS be better than ours?
Huh!🤔UConn’s opponents Ohio St., Louisville, MI and FSU have SoS as 8, 9, 10 and 12, respectively. Others are Utah (31) and Loyola (72).
TX’s opponents UCLA and South Carolina have SoS as 4 and 6, respectively. Others are Richmond (26), James Madison (32), Incarnate World (134), Louisiana-Lafayette (178), and Texas-Southern (359).
Massey’s undergrad thesis normally solves an n x n system of equations for the Ratings (maximum likelihood Bayesian estimation).
If I am reading this FAQ correctly, the pure Margin of Victory (MOV) model of his thesis (above) is proprietarily adjusted so that the MOV goes through a optimized normalization function that transforms the MOV to a number between 0 and 1 that is then input in the n x n equations. The transformation accounts for blowouts, venue and implicitly for strength of schedule (SoS). The “optimization” solves for the “equilibrium” transformation (per some measure) that yields Ratings and SoS.
Thank you. Now I understand.UConn’s opponents Ohio St., Louisville, MI and FSU have SoS as 8, 9, 10 and 12, respectively. Others are Utah (31) and Loyola (72).
TX’s opponents UCLA and South Carolina have SoS as 4 and 6, respectively. Others are Richmond (26), James Madison (32), Incarnate World (134), Louisiana-Lafayette (178), and Texas-Southern (359).
Massey’s undergrad thesis normally solves an n x n system of equations for the Ratings (maximum likelihood Bayesian estimation).
If I am reading this FAQ correctly, the pure Margin of Victory (MOV) model of his thesis (above) is proprietarily adjusted so that the MOV goes through a optimized normalization function that transforms the MOV to a number between 0 and 1 that is then input in the n x n equations. The transformation accounts for blowouts, venue and implicitly for strength of schedule (SoS). The “optimization” solves for the “equilibrium” transformation (per some measure) that yields Ratings and SoS.
Stability is nice but not at the expense of on court proof. That ranking has undefeated Texas ranked below SCar and UCLA whom the Longhorns have defeated. It's a good site though, and does provide a lot of good data used by the algorithm.I like Bart Torvik's better (barttorvik.com). Seems to have stability in the rankings. No huge changes from 1 game to the next.
As Massey continues to integrate this season’s data, UConn has just surpassed South Carolina in the Power column. This calculation changes more slowly than Rating as SC has been #1 since last season, even after UConn won the championship.
Torvik's method doesn't rely heavily on the outcome of one game. I believe his methodology tries to give in-site to what would be the outcome if the teams played 100 times. In the games you mentioned, his system probably "thinks" that SCar and UCLA would still beat Texas maybe 51 times out of 100. Texas just happened to take the 1st actual game.Stability is nice but not at the expense of on court proof. That ranking has undefeated Texas ranked below SCar and UCLA whom the Longhorns have defeated. It's a good site though, and does provide a lot of good data used by the algorithm.
I look at most of the statistical sites, and like the one you referenced too, in particular their Plus/Minus rating per 100 possessions, adjusted for level of competition can be viewed for each player game by game, not just for the entire season.I like Bart Torvik's better (barttorvik.com). Seems to have stability in the rankings. No huge changes from 1 game to the next.