Posts Tagged ‘big ten’

I [heart] Tom Izzo

Don’t mess with the Big Ten:

If you’re a great defensive team, you aren’t going to score as many points just because you don’t have as many possessions, because it’s going to take– if you’re a great defensive team and a great offensive team, you still only get so many shots, you’re only going to score so many points. It’s going to take people 30 seconds, because you’re not going to give up the layup on the fast break, we’re going to make them earn it. If it takes them 30 seconds to score instead of 10, that’s 20 less seconds we get the ball back.

“So you can’t get deceived on what great offense and great defense is. At times have we’ve been – favorite word – dysfunctional, whatever words you guys want to use. Yeah, I’m the one that said it. I said our offense isn’t as smooth because guys aren’t maybe together on it. We’re not practicing with the same guys. We’re playing 100%. But this little bit of national perspective and everything on the Big Ten, let me see now, it’s been 11 years. We’ve been to five. There’s been four or five other teams. In 11 years, there’s been pretty good representation in the greatest game of all, the greatest weekend of all, that’s the Final Four.

Michigan State’s Tom Izzo defends Big Ten’s style of play

HT on the video clip: UMHoops.


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Allow me to say . . .


Unfortunately, I’m going to guess Digger Phelps will be taking a different methodological approach.

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OK, it’s already “later tonight” and the all-Big Ten selections have been released.

Your Spartan awardees:

  • Kalin Lucas was selected as the conference Player of the Year–and a first-team all-conference pick–by both the coaches and the media.  Chalk up another rock-solid preseason prediction for the all-knowing, all-seeing Spartans Weblog.  (You’ll recall that Lucas didn’t even make the preseason all-conference team.)
  • Tom Izzo was named Coach of the Year by the coaches; Ed DeChellis was picked by the media.  (If he had to pick only one, I’d think Izzo will value being selected by the coaches more than he would be by the media.)
  • Travis Walton was named Defensive Player of the Year by both the coaches and the media.
  • Goran Suton was named to the all-conference second team by both the coaches and the media.  (I was ready to throw a full-size blogger hissy fit if this didn’t happen.)
  • Raymar Morgan was granted honorable mention status by both sets of voters; Walton received that status in the media voting only.
  • Delvon Roe was named to the All-Freshman Team.

That’s a very nice haul of awards–appropriate for a team that finished in first place by 4 games.

I have to say, I have almost no gripes with any of the selections made by either the coaches or the media.  I had jotted down a rough draft of the three all-conference teams in a meeting earlier today–and the names match almost exactly what I had.

The first team (Harris/Lucas/Turner/Battle/J. Johnson) seemed pretty clear to most observers, I think.  Manny Harris and Talor Battle both struggled some in conference play (Harris more than Battle), but both were very, very good in nonconference play and both made some huge plays to get their teams into NCAA Tournament position down the stretch.

You can argue what order to put the 10 guys who made the 2nd/3rd teams in (reflecting the league’s parity this season), but the list is a solid one (click through to see it).  The only name that jumps out at me as out of place is E’Twaun Moore.  For a guy who’s only real role is to score, a shooting line of .487/.333/.778 doesn’t impress.  But I don’t necessarily have a better pick.  (Surprisingly, this turned out to be a better year for post players than for perimeter guys.)  Walton might deserve a spot, but the all-conference teams tends to focus on offense, with the defensive awards as a consolation.

On that note, you can’t argue with one iota of the All-Defensive Team (Frazier/Walton/D. Johnson/Kramer/J. Johnson).  Walton and Damian Johnson would both have made deserving Defensive Players of the Year.  And Gatens/Roe/Buford/Mullens/Jackson is a good looking All-Freshman Team.

In short: I’m glad I didn’t too much time compiling my picks, as it would have been an exercise in redundancy.

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Rankings Update

Question (that I do not know the answer to): Could we be placed in the Midwest region (final in Indy) as a #2 seed?  It seems like I remember similar situations occurring in past years, but the couple of bracket projections I looked at today (including Lunardi’s) show us elsewhere.

Izzo for Big Ten Coach of the Year?

The Detroit News’ Eric Lacy has a blog post up making the case for Tom Izzo as the conference’s coach of the year.  Key segment:

Player injuries and illness have forced Izzo to use 13 different starting lineups, as well as play three freshmen (Korie Lucious, Delvon Roe and Draymond Green) key minutes.

Izzo’s team is 23-5 (13-3 Big Ten) despite playing one of the nation’s toughest schedules and they are an NCAA-best 11-2 against the top 50 teams in the RPI.”

Beat the Indiana Hoosiers on Tuesday and the program earns its first outright conference championship since the 1998-99 season.

It’s an uncoventional nomination.  Generally, high-profile coaches of teams expected to compete for the conference title are only considered for conference coach of the year if they put together a truly dominant conference record.  In this case, though, the team in question has played nearly half its conference schedule (eight games) with its preseason all-conference player missing or severely limited and nevertheless put together a title-winning record while losing just one game on the road.  It’s hard to do that without some stellar coaching along the way.

The conference coach of the year race is a lot like the conference player year of the race: There are plenty of plausible candidates, with no clear front runner.  Really, you could make an argument for any of the guys whose teams have increased their number of conference wins from last year:

  • Bruce Weber (+6): From second division to title contender–except that their fundamental performance really hasn’t improved.
  • Bill Carmody (+6): Do you give him credit for how close they’ve come to a winning conference record or hold the late-game collapses against him?
  • John Beilein (+3): From 10-22 to 18-12 with basically the same talent.
  • Ed DeChellis (+2): Built an upper-division team around two undersized stars.
  • Tom Izzo (+1)

What do you guys think?

Indiana Game Preview

7:00 Tuesday.  Assembly Hall, Bloomington, Indiana.  ESPN.

There was certainly very little time for the players to celebrate clinching a share of the Big Ten title.  In fact, our Spartans didn’t even have time to come home–going straight from Champaign to Bloomington.  Thankfully, the extra day of rest/preparation is the only advantage Indiana brings into this game:

Category MSU Off Rk IU Def Rk
PPP 1.07 1t 1.11 11
TO% 21.6 7 19.2 8
eFG% 48.7 7t 56.3 11
FTR 38.5 1 38.8 11
OffReb% 42.8 1 28,8 4
Category IU Off Rk MSU Def Rk
PPP 0.93 11 0.94 2t
TO% 25.6 11 20.7 3t
eFG% 48.2 9 48.1 4
FTR 34.5 4 34.3 7
OffReb% 33.3 3 24.7 1

What I said about the numbers prior to the last meeting:

The rebounding numbers, I think, reflect this is a team that works hard and hustles in a league that doesn’t place much emphasis on offensive rebounding.  The 3-point shooting is more impressive, given that they don’t have any quality inside scoring options to draw defenders in; their 2-point shooting percentage of 43.1% is only slightly higher than their 3-point shooting percentage.

Offsetting those strengths are a multitude of weaknesses.  A high turnover percentage 24.4%) and opposing free-throw rate (39.9%) indicate they’re overmatched defensively.  And they’re allowing opponents to shoot the same 41.3% on 3-pointers.

Three-point shooting is, of course, the one great hope of underdogs facing long odds.  Earlier in conference play, IU looked like it was emerging as a serious 3-point shooting threat, hitting over 50.0% of their 3-point attempts in 4 consecutive games (culminating in their single conference win, against Iowa).  Since then, however, the Hoosiers have shot just 29.6% from 3-point range over 7 games.

Devan Dumes had been the main source of the torrid 3-point shooting numbers, making 18 of 29 long-distance shots in the 4-game stretch.  The next game was against us.  He did a bad, bad thing in that game, was suspended by Tom Crean for two games, and has hit a pedestrian 7 of 21 three-point attempts in the four games since.

Verdell Jones III has been Indiana’s leading scorer of late, scoring 59 points in the 4 games since these two teams met in East Lansing.  At 6’5″, Jones does the vast majority of his scoring from inside the 3-point arc.

On the other end of the court, this game might represent a chance for MSU to refind its own 3-point shooting stroke.  Seven of IU’s conference opponents have hit the 50.0% mark from beyond the arc.

Kenpom predicts a 71-59 MSU win in a 67-possession game.  The conventional thing to say here is that we can’t take anything for granted–and point out that the Hoosiers played Penn State to the wire on the road on Saturday–but I just really can’t see this MSU team losing this game under anything but the most bizarre circumstances.  Indiana has lost 18 of the last 19 basketball games it’s played.  If ever there was a time not to take a bad team too lightly, this is the time.  An outright conference title awaits.

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Rankings Update

Purdue is ranked one spot ahead of us in both the human polls, reflecting that we’re now basically back to being dead even with them.  Kenpom currently projects a 13-5 conference finish for both teams, with Illinois (12-6) and Minnesota (11-7) also in the mix.

I’ve added Crashing the Dance to the list.  As you’ll recall from last year, the site uses quantitative methods to try to predict the behavior of the NCAA Tournament Selection Committee based on past results.

Monday Night Links

Conference Midseason Review: The Teams

Here’s your up-to-the-minute, conference-only tempo-free aerial:

b10 tfa feb2

I’ve used 1.03 points per possession–the conference average to date this season–as the midpoint for each axis.  While the 10 non-IU teams have sorted themselves out a lot more neatly than they did in nonconference play, no team has grabbed the mantle of “solidly above average on both ends of the court.”  MSU has the best offense in the league, but is basically average on defense.  Purdue and Illinois have been the class of the league defensively, but mediocre on offense.  Same deal, with a somewhat less stout defense, for Minnesota.

The simplest way to frame the conference race from a statistical standpoint is this: Which happens first in the second half of league play? MSU playing improved defense or Purdue scoring more efficiently?  Can one (or both) of them move their dot into middle of the upper, right-hand quadrant?

The two big surprises relative to nonconference performance are:

  • Ohio State, which has leapt from the good defense/bad offense quadrant to the good offense/bad defense quadrant (they’re currently exactly where Penn State is).  The improvement in offense has been fueled by the development of freshmen B.J. Mullens and William Buford.  On defense, opponents are making 38.2% of their three-point attempts–not good for a team that tries to force perimeter shots with its zone defense.
  • Michigan, which has gone from the being best offensive Big Ten team in nonconference play, by a healthy margin, to hanging out in tempo-free land with the Hawkeyes and Wildcats in.  (More on that below.)

Final note: While conference-only data are the analytical ideal, my sense is that the midseason data are less reliable than they might have been in years past.  It used to be that you played nine different teams in your first nine games, as the conference employed an out-and-back scheduling scheme.  For whatever reason, teams now regularly play the same oppnent twice in the first half of the schedule.  MSU, for example, has already played Northwestern, Ohio State, and Penn State twice each–meaning they’ve played only 6 of 10 total conference opponents to date.  Given that all three of those teams are below-average on defense, MSU’s offense may not be quite as dominant as the numbers currently indicate.

Conference Midseason Review: The Players

Here’s your Spartans Weblog Midseason All-Conference Team, based exclusively on in-conference stats/performance:

  • Talor Battle (Penn State)
    18.7 points/game, 39.0% 3pt%, 44.0% FT rate, 5.0 assists/game, 2.4 TOs/game
    I don’t think anyone who saw Sunday’s game needs me to throw any more superlatives Battle’s way.  The conference player of the year to date.
  • Kalin Lucas (Michigan State)
    19.2 points/game, 38.2% 3pt%, 46.7% FT rate, 3.7 assista/game, 2.4 TOs/game
    Assists are down, but scoring is way up since the nonconference season.  Shooting a very good 46.2% on 2-pointer given the number of shots he takes late in the shot clock (well above the 40% threshold I set for him during his early-season slump).
  • Lawrence Westbrook (Minnesota)
    15.0 points/game, 57.8% eFG%, 88.9% FT%, 1.6 TOs/game
    Westbrook has been a model on consistency for a Gophers team that was looking for a go-to player going into the conference season; he’s scored in double digits in every conference game.
  • Goran Suton (Michigan State)
    10.7 points/game, 60.0% eFG%, 9.9 rebounds/game, 13.8 OffReb%, 27.0% DefReb%
    I’ll confess to a bit of homerism here.  But Mr. Suton has been utterly dominant on the glass, ranking 2nd in the league in offensive rebounding percentage and first in defensive rebounding percentage.
  • JaJuan Johnson (Purdue)
    12.8 points/game, 53.8% 2pt%, 70.7% FT rate, 7.4 rebounds/game, 11.2% OffReb%, 10.1% Block%
    The best all-around post player in the league, despite having to play surrouneded by four guards for large stretches of time.

Battle is the only returnee from my pre-conference season all-conference team, although you could make a pretty good case for Robbie Hummel (despite missed time due to his back issues) and Evan Turner.  Battle, Lucas, and Johnson are the only first-team locks.  Westbrook just edged out Northwestern’s Craig Moore (40.0% on a league-leading 82 three-point attempts).

As for the two other players on the pre-conference season version of the team, the numbers are not as pretty as they once were:

  • Manny Harris: 14.0 points/game, 41.1% eFG%, 7.2 rebounds/game, 3.3 assists/game, 3.9 turnovers/game
  • DeShawn Sims: 13.3 points/game, 48.3 eFG%, 5.9 rebounds/game, 8.6% OffReb%, 17.2% DefReb%

Harris has basically reverted to the freshman version of himself statistically (except for a big jump in rebounds).  Sims’ production hasn’t plummeted quite as far, but his 2-point shooting percentage has dropped 9 points and he’s lost 2-3 percentage points on his rebounding percentages.  Without these two guys playing at the stratospheric levels they achieved during nonconfernce play, Michigan’s offense has fallen to NIT-quality levels, if not below.

Coffee Talk: Who’s impressed you the most in conference play to date (teams or players)?  Who did I miss on the all-conference team?  Does anyone out there (besides his mother) love Goran Suton as much as I do?

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Points Over Replacement Per Adjusted Game

Baseball statistics were my first love, so I thought it would be fun to cook up something as obscure as VORP or BABIP.

But let’s back up:

There was some conversation following my last scatterplot post about how to appropriately interpret the graph in terms of which players have played most effectively on offense.  The individual player offensive rating/usage rate scatterplot isn’t as easy to interpret as the team offensive/defensive efficiency scatterplot.

With offensive rating and usage rate, you really need to multiply the two numbers together (as opposed to subtracting defensive efficiency from offensive efficiency to get efficiency margin).  Taking this concept a bit further, SpartanDan came up with the following:

I wonder if the best measure might be something like (ORtg – 90) * (%Poss), somewhat analogous to baseball’s VORP (value over replacement player).  Not many players below 90 get significant playing time in the major conferences (in the Big Ten, there are eight with 30% or higher minutes and below-90 ORtg, but five play for Indiana), so 90 might be considered “replacement level”.  This formula would give the increase in team points per 100 possessions relative to having a player with ORtg 90 taking all of your possessions.

Breaking this down mathematically:

  • Offensive rating (OffRtg) is basically points produced per 100 possessions used.
  • Usage rate (%Poss) is possessions used per 100 possessions played.
  • So multiplying the two gets you points produced per 100 possessions played.
  • By using (Offensive rating minus 90), you get points over replacement level per 100 possessions played.

So far, all the credit here goes to SpartanDan.

Here’s my addition: If you want to look at which players have contributed  the most marginal offensive value for their teams, you really want an absolute value, not a rate statistic.  If Player A can play 35 minutes per game at a given efficiency/usage level, while Player B plays only 25 minutes per game at the same levels, Player A is contributing more to his team’s efforts to win a given game, since Player B’s team has to find another player (presumably a less efficient one) to play the extra 10 minutes.

Here’s the equation for PORPAG:

(OffRtg – 88) * %Poss * Min% *65


  • I’ve tweaked “replacement level” down to 88.  That’s the average of the 9th best offensive rating on each Big Ten roster at the moment.  (In some cases, the 9th best rating was really, really low, in which case I subtracted 5 from the 8th best rating.)
  • Min% is the percentage of a team’s total minutes a player has played.  Games missed due to injury drive that percentage down.
  • 65 is the current average adjusted tempo for the 11 Big Ten teams.

If my math is correct, this equation gets you something like “Marginal offensive points contributed per game, accounting for a team’s average pace.”


  • Offensive rating accounts for basically all the offensive statistics we have, but can’t cover everything that happens on the court (setting picks, intangibles, etc.).
  • The numbers obviously say nothing at all about a player’s defensive contributions.
  • The “replacement level” concept works better in baseball–where swapping out one player for another in the batting lineup or pitching rotation is a pretty simple change–than it does in basketball–where swapping one player for another alters the team’s on-court dynamics.  But that’s the nature of basketball statistics.
  • We’re using data for both nonconference and conference games, so the numbers reflect individual offensive performances against differing levels of opposition.  Ideally, we’d do this at the end of the year using conference-only data (at which time we’d want to revisit the replacement-level/average-pace assumptions).

OK, so here’s what this approach gets us.  I’ve calculated PORPAG for the top 30 per-game scorers in the league:

Player Min% OffRtg %Poss PORPAG
Battle (PSU) 91.6 121.5 26.6 5.46
Harris (MICH) 81.2 113.0 32.5 4.46
Lucas (MSU) 76.0 121.3 23.0 3.90
Gatens (IOWA) 78.2 127.2 18.9 3.86
Pringle (PSU) 69.6 121.3 23.0 3.57
Sims (MICH) 76.8 115.8 24.8 3.57
Hummel (PUR) 63.5 125.7 20.8 3.32
Meachem (ILL) 75.7 128.1 16.4 3.32
Moore (NW) 87.7 117.2 18.5 3.18
Hughes (WIS) 77.1 114.9 21.6 3.02
Turner (OSU) 84.8 106.5 27.3 2.93
Morgan (MSU) 67.3 112.7 25.6 2.88
Bohannon (WIS) 77.2 115.7 19.5 2.81
Johnson (PUR) 60.5 117.6 23.3 2.80
Coble (NW) 84.4 109.7 22.4 2.79
Landry (WIS) 77.1 110.1 23.1 2.67
Davis (ILL) 70.5 111.6 20.9 2.36
Peterson (IOWA) 78.5 105.5 23.3 2.20
Diebler (OSU) 83.9 109.3 17.7 2.15
McCamey (ILL) 71.4 104.9 24.6 2.04
Westbrook (MIN) 54.1 107.6 27.3 1.98
Cornley (PSU) 85.0 102.5 22.6 1.94
Leuer (WIS) 48.2 107.2 28.3 1.79
Tucker (IOWA) 39.9 115.3 23.8 1.75
Buford (OSU) 55.9 107.8 22.7 1.72
Allen (MSU) 49.8 108.9 24.0 1.70
Tisdale (ILL) 57.7 103.5 24.5 1.52
Pritchard (IU) 73.6 98.0 24.6 1.29
Moore (PUR) 78.5 95.7 26.3 1.17
Dumes (IU) 70.2 89.0 26.9 0.25

I think these results are, for the most part, pretty intuitive.  Talor Battle, Manny Harris, and Kalin Lucas would be at the top of almost everyone”s player of the year ballots right now.  Remove any of them from their respective teams’ lineups and you’d expect team scoring to go down by 4-5 points per game.

At the other end of the list, removing E’Twaun Moore (the way he’s been playing this season, at least) or Devan Dumes from their team’s lineups would have a pretty negligible impact.

If anything, the system probably overvalues offensive rating relative to usage rate.  Matt Gatens, Stanley Pringle, Trent Meachem, and Craig Moore all rank in the top ten on this list largely because they’re good 3-point shooters (although Pringle’s got a healthy usage rate).  It’s hard to separate out how much credit should to go the 3-point shooters versus the other guys on the team who set the picks and made the passes to get them the open looks.

Meanwhile, Evan Turner and Raymar Morgan–two players with decent, but not great, offensive ratings and pretty high usage rates–slide down the ladder relative to their per-game scoring averages.  The preseason conference player of the year, Robbie Hummel, doesn’t rank in the top five as a result of the minutes he’s missed due to back problems.

Anyway, I’ll be interested to hear if these statistical gymnastics make sense to others.  On one hand, one hates to manipulate tempo-free stats too much.  I think the main reason that advanced basketball stats have caught on in the mainstream more quickly than advanced baseball or football stats is that they’re more elegant.  On the other hand, I think the result of the manipulations we’ve done here is a pretty intuitive one, answering the question “How much is this guy contributing on offense each game?”

Final note: To the extent there’s value here, the bulk of the credit goes to Dan.  And, as the academics say, the responsibility for any errors rests solely with the author.

Update: I’d forgotten that Dean Oliver calculates individual win-loss records in “Basketball on Paper” (using a more complex methodology that looks at defense, too).  Here’s an explanation of calculating “Win Shares” for the NBA.  And here’s an ACC blogger who developed a formula for calculating Wins Over Replacement Player.  So others have attempted to cross this river before.

Personally, I like the points-based stat; pushing things to the win-loss level seems a bit too much at the college level, where the quality of your opponents varies so much across the season.  And there are, of course, issues with individual defensive ratings; beyond steals and blocks, you’re basically just divvying up team defensive performance.

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With the offensive rating/usage rate scatterplot phenomenon rapidly spreading through the Big Ten blogsophere (OK, one other guy decided to make one), I was inspired to produced another scatterplot of my own.  Here it is:

The scatterplot includes the top two scorers on every Big Ten team (except for IU’s Devan Dumes, whose offensive rating of 89.0 doesn’t show up on the scale I’ve been using).

I won’t comment extensively, except to note that the graph confirms the reason I like this approach: it identifies the best offensive players in a way that’s intuitive.  I think most observers would say Robbie Hummel, Talor Battle, Kalin Lucas, DeShaun Sims, and Manny Harris have been the top offensive performers in the league this year.  And those are the five players nearest the upper, right-hand corner on the scatterplot.

I apologize for how cluttered the graph is.  Raymar Morgan’s datapoint is the one to the right of his name.

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