Next Up - Western Carolina

Teams: Western Carolina Catamounts vs. Iowa Hawkeyes
Site: Carver-Hawkeye Arena, Iowa City
Time: 7:05 CST, Saturday

National rankings in parentheses, when available.

.........................Western Carolina..........Iowa
Record.........................5-6........................8-1
Pts/G........................75.3 (84)................82.6 (21)
Pts All/G....................77.7.......................71.2
Poss/G......................74.5 (22)...............72.1 (56)
Off. Efficiency..........101.1 (200)............114.5 (30)
Def. Efficiency.........104.3.......................98.8
FTA/FGA..................0.403 (86)..............0.431 (52)
3PA/FGA..................0.252 (296)............0.334 (148)
FG%.........................0.441 (165)............0.482 (45)
3FG%.......................0.272 (310)............0.420 (21)
adjFG%....................0.475 (223)............0.552 (37)
FT%.........................0.627 (262)............0.709 (97)
A/TO..........................0.88 (177)..............1.27 (24)
TO/100 poss.............21.9 (93)...............19.7 (47)
Pomeroy Rank..............289........................20
Sagarin Rank................275........................10

Random Notes
Western Carolina might be Iowa's last easy victory for at least a couple games, as they follow this one with Texas Tech (in Chicago), Air Force, and St. Louis before starting Big Ten play. WCU lost by 15 to Penn State earlier, 31 to Alabama, and 11 to Marquette. Three of their five wins are against non-Division I teams. Western Carolina's stats (other than the record) are based on their first 10 games, which includes two of the non-D-I teams, so they're a little inflated.

An opponent that plays a fast game and doesn't play good defense - now that's music to Iowa's ears. I'm not sure if Western Carolina's high poss/g comes from an inherently fast style or from playing fast teams that force it to run, but either scenario should spell lots of points for the Hawkeyes. Iowa's possessions per game mark leads the Big Ten, and we've seen that they like to run the floor when given the opportunity.

WCU's 6'8" Rans Brempong averages 5.6 blocks/40 min, which is very good, but not quite Erek Hansen (6.8) good. Brempong is also shooting 67% on the year while averaging 11.5 ppg. David Berghoefer, a 6'11" center, leads the team in points (15.2) and rebounds (6.8). He scored 23 in the game against Alabama, 19 against Penn State, and 20 against Marquette. Is Iowa ready to defend a big man? Jared Homan (ISU), Eric Coleman (UNI), Aliou Keita (Drake), and Chad Maclies (Centenary) say otherwise.

The Catamounts don't shoot many threes, relatively speaking. That's probably good strategy since they're only making 28% as a team. Iowa is right near the national average for threes taken per FGA.

Jeff Horner added some well-deserved hardware to his mantel yesterday when he was chosen for the Big Ten's All Wonk Team. Congratulations, Jeff.

New Feature
Always seeking more reader feedback, I thought it might be fun to try a "Pick to Click" contest (anyone who's seen a White Sox game announced by Ken Harrelson and Darren Jackson knows what I'm talking about). Simple idea - you pick a player before the game, and whoever chooses the player that plays the best that night gets, well, bragging rights. Here's your chance to humble my intelligence. Some friends and I usually do it before going to games; it gives you an additional rooting interest if nothing else.

On that note, I select Adam Haluska for my Pick to Click tomorrow night. He started the year a little slowly after working back from his hip flexor injury, at times looking tired and out of sync. Once he got over that, though, he's been able to score in bunches and provide great defense. Some people knock his three-point shooting, which started slowly with the rest of his game, but he's been hot of late, climbing to 40.5%. During Iowa's current 5-game win streak he is shooting a very respectable 12-26 (46%) from behind the arc.

If you'd like to make a Pick to Click, submit your selection, and if you like, a short explanation for it, between now and 7 CST tomorrow night in the comments following this post. Email entries are also acceptable. Making fun of the Hawk and DJ is encouraged, especially with phrases like "Stretch!," "Hegawn!," "Can 'o corn," and "You can put it on the boooard....Yes!" If you leave your screen/user name with your choice, I'll keep a running tally of who makes the most correct selections on the season. Winners will be determined by the Hawkeye Hoops Player of the Game (see sidebar), so to avoid any conflict of interest, my selections won't be included in any season-long competition (which in all likelihood means there will be 0 competitors, heh).

Enjoy the game.

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Stat of the Day, Pierre Pierce Edition
Today I want to look at the players who miss a lot of shots, stay in the lineup for one reason or another, and continue to miss a lot more shots. It'll be very similar to the Hacking Mass contest that Baseball Prospectus runs every year. Funny quote from their contest guidelines for choosing a team:

In each case, it isn't enough for a player to simply suck; somehow the Stiffest
of the Stiff must find a way to remain in the lineup or rotation. Possession of
incriminating photos of managers and GMs, telekinesis of ink onto lineup cards,
large contracts that need justification, and ties to the underworld can all be
important attributes of your players besides their lack of hitting and pitching
talent.

This shouldn't require too many underlying stats to calculate. We're looking for guys that miss at a high rate (a FG% of some type) and who shoot a lot (FGA or misses). I used the following formula:

[(1.5 - adjFG%) * (FGA -FG)] / games

New to adjFG%? (Otherwise, skip ahead)
*************************************************
adjFG% gives a little extra credit to 3-pt shooters to make up for the fact that they can shoot a lower FG% and still be as effective as a higher FG% post player. In other words, a guy shooting only threes and making 33% of his shots will score as often as a guy shooting only twos who makes 50% of them. adjFG% gives us a number to more accurately compare how efficient different types of players are from the field.

ajdFG% = (FG + 0.5*3PM) / FGA
*************************************************

At first I wasn't sure if I should multiply by field goal attempts or by misses. If you use attempts, a guy who makes a shot could still see his score go up (higher is worse) because it would add to his attempts total (although it would increase his adjFG% and offset some of the increase). I chose to multiply by misses to avoid some of what I just mentioned. This way, a player increases his rating whenver he misses and decreases whenever he makes a shot. In either case, the results aren't very different.

On this site, today's fun number will be called the Pierre Pierce Rating (PPR), in deference to his willingness to lead his team in shots while trailing at least 5 teammates in shooting effectiveness. (Could be more, could be less, depending on how far down the roster you want to go. I stopped at guys with 15+ mpg.) By the way, field goal attempts aren't even close on this team. Pierce has 52% more of them than Horner, the second highest (131 to 86).

Side note - I wanted to name this after a former Hawkeye, but my memory doesn't have too many years to span, and I don't really remember shooting percentages of guys who I watched growing up, so if anyone has a suggestion for a different namesake, please drop me a line.

Here we go.

Big Ten Leaders, PPR (thru Tues)

Player, Team	GP	adjFG%	FGA-FG	PPR
David Teague, PUR	5	0.319	61	14.41
Dion Harris, MICH	10	0.380	95	10.62
Brandon McKnight, PUR	7	0.450	61	9.15
Pierre Pierce, IOWA	9	0.477	75	8.52
Vedran Vukusic, NW	6	0.494	50	8.38
Bracey Wright, IND	6	0.517	51	8.35
Robert Vaden, IND	6	0.431	44	7.84
Daniel Horton, MICH	7	0.466	46	6.80
Deron Williams, ILL	9	0.505	54	5.97
Dan Coleman, MIN	8	0.469	46	5.93

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Stat of the Day, Jess Settles Edition
To paraphrase another blogger's recent paraphrase of an idea from Moneyball - a useful statistic is one that helps clarify things we think we know, point out things we didn't know or were wrong about, and provide context for debates. Today's idea started under the first category, as I wanted to clarify a conjecture of mine. (By the way, I recommend this interview with Moneyball author Michael Lewis, especially if you're into investing. It's a five-parter, so you have to scroll down to the bottom and click on Part I to start at the beginning.)

I was looking ahead to next year thinking that Michigan State, Illinois, and Wisconsin each have rosters that feature seniors or players who could turn pro, or both. Meanwhile, Iowa plays mostly juniors and should have every significant contributor back next year, and I suspect they'll be at least as competitive as they are this year. I have no reasonable means of quantifying a player's likelihood to leave early for the NBA, but measuring a team's collective age doesn't seem too difficult.

The simplest way to measure age would probably be to assign weights to each class (e.g., 1 = frosh, 2 = soph, etc), and average for the number of players on the roster. That's a little too simple since it doesn't account for playing time, meaning a guy playing 5 mpg would factor in just as much as a guy playing 35 mpg.

Here's the approach I took. I assigned each class the following numerical value - frosh = 1, soph = 2, junior = 3, senior = 4. Easy enough. Then I multiplied each player's total points scored by his class value. Call the new number weighted points (WP). I divided the sum of a team's WPs by the team's total points to arrive at an index that approximates the relative age of a team, based on the scoring contributions from each class. A score of 1.00 means a team gets all its points from freshmen, while 4.00 signifies all scoring from seniors.

Here are the calculations for Iowa to get you started.

Jeff Horner, Jr, 3 x 145 pts = 435 WP
Pierre Pierce, Jr, 3 x 143 =429
Adam Haluska, So, 2 x 120 = 240
Greg Brunner, Jr, 3 x 120 = 360
Erek Hansen, Jr, 3 x 74 = 222
Doug Thomas, Jr, 3 x 43 =129
Mike Henderson, So, 2 x 42 =84
Carlton Reed, Fr, 1 x 34 = 34
J.R. Angle, Fr, 1 x 8 = 8
Alex Thompson, Fr, 1 x 8 = 8
Jack Brownlee, Sr, 4 x 3 = 12
Justin Wieck, Jr, 3 x 2 = 6
Seth Gorney, Fr, 1 x 1 = 1

(Total WP) / (Total Points) = 1968 / 743 = 2.65

Sorry for the sloppiness, but I think you get the idea.

Since this stat purports to measure "age," I've chosen Jess Settles as its figurehead. Yes, Father Time himself, the former sixth-year senior who was harrassed with chants of "GRAND-PA SET-TLES!" in a game at Michigan State.

Jess Settles Sidenote - He definitely fit the classic "Iowa's Favorite Son" mold, didn't he? By that I mean a former farm/small town kid turned successful athlete - guys like Robert Gallery and Tim Dwight. (OK, Dwight is neither farm boy nor small town Iowa, but Iowa City classifies as "small town" for anyone who's spent time outside of Iowa, and he's the first guy I think of for most popular Iowa athletes of recent years. Never knew he has a yoga studio in town.) Just curious - Jared Homan, dairy farmer turned NBA prospect, meets the criteria, so why doesn't he get any affection?

Without further ado, here is the Big Ten, as arranged from oldest to youngest according to the Jess Settles Index (JSI).

Team.......................JSI
Illinois......................3.29
Northwestern..........3.25
Michigan State........3.16
Wisconsin..............3.10
Purdue....................3.07
Ohio State..............2.99
Minnesota...............2.76
Iowa........................2.65
Michigan.................2.35
Indiana....................2.17
Penn State..............2.06

Thoughts
- Wow, Penn State is playing some pups out there. Their list of 20+ mpg players includes 3 freshmen, 2 sophomores, and 2 juniors.
- 4 of Wisconsin's top 5 scorers are seniors, but their rating drops a little since most of their other players are freshmen and sophomores.
- Michigan State has 3 seniors playing at least 22 mpg.
- Illinois's top five is comprised of 2 seniors and three juniors. An NBA defection or two could really improve Iowa's prospects next year.
- Michigan has 11 guys who average at least 10 mpg (there would be fewer without the pile of injuries), and none are seniors.
- I also measured age by weighted minutes played, but the results weren't significantly different.

Of course it's too early in the season for this stat to be very conclusive, as many teams are playing deep into their lineups while they feast on cupcake opponents. Team points will be concentrated among fewer players as we get further into the season. I'll be updating the Jess Settles Index in a post toward the end of the year (if I remember, of course).

Also, I'm kind of curious to see if there's any correlation between team age and performance statistics, such as winning percentage, offensive/defensive efficiency, shooting percentages, etc. I've put up with the maxim of "you need veteran leadership to win in the tournament" without any skepticism for far too long. It's about time I actually looked into it. I've been working on database to compile numbers for teams and players nationwide for a while, and a productive winter break might give me a chance to explore this idea as well as several others.

Stay tuned.

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Stat of the Day, Kent McCausland Edition
Chas from Pitt Sports Blather posed an interesting thought last night at the College Basketball Blog. He said that his preference of basketball teams was affected by his personal style of play on the court.

What game I have is on the defensive end. I can take a charge, pester, get guys
annoyed, actually force guys out of position inside and I'm willing to dive for
balls.And that's the kind of basketball team I like. The teams that stress
defense first. (Very convenient, since that has been Pitt's approach in the last
5 years.) I'm not talking thug basketball like what Riley and the Knicks did in
the 90s. I'm talking about sound, fundamental game where you make the other team
work to find a good shot.

I'm not sure if it's the same for many others, but the teams and players I like seem to be similar in style to the way I used to play. I was the just-a-little-too-short guy who stuck to the perimeter because I had a decent shot and was of no help at all with interior defense. I tend to like teams and players that are effective long-range shooters, even if they have defensive shortcomings (see: Iowa Hawkeyes, Jake Sullivan).

High School Flashback (I'm warning you now - feel free to skip ahead)
I was sitting in my customary position near the end of the bench. When the third quarter ended with our team safely ahead, Coach gave me the nod to start the fourth quarter at point guard. The varsity game that followed ours was against our biggest rival, who featured eventual D-1 player David Rottinghaus (Wyoming), so our tiny gym was already packed when I took the floor. Our opponents apparently forgot to scout our third-stringers, as they repeatedly left me open from downtown. I made them pay, not once, not twice, but three times from behind the arc. I finished the quarter 3-3 on three-pointers and contributed 12 of our team's 97 points (don't forget, that's in a 32 minute game). Good times. I wish there was a happy ending, like me getting bumped up into the rotation or filling in for an injured player or something, but nope, I finished that season with a grand total of 14 points.

(It's safe to start reading again)
Anyway, all of this got me interested in seeing which Big Ten teams were most three-happy, and which players loosely resembled my playing days of yesteryear. We'll measure this with the ratio of 3FGA to FGA. I'll term it the Kent McCausland Percentage (KMP), in honor of the former Hawkeye guard who once led the nation in 3FG%, but rarely stepped inside the arc. Enjoy.

Team Leaders, 3FGA / FGA (KMP)
Northwestern....................45.3%
Indiana...............................41.4
Wisconsin.........................38.4
Illinois................................36.6
Ohio State.........................36.3
Penn State........................35.5
Iowa..................................33.4
Michigan State..................33.1
Purdue..............................30.5
Michigan............................30.0
Minnesota..........................26.0

Just for reference, the Big Ten average is 34.8%, and the national average is 32.5%.

Individual Leaders, 3FGA / FGA (min 15 mpg)

01. Rich McBride, ILL.............88.6%
02. Aaron Robinson, MIN........82.2
03. Chris Hill, MSU..................77.4
04. Je'Kel Foster, OSU...........73.9
05. Mike Walker, PSU.............72.4
06. Sharif Chambliss, WIS.....68.9
07. Clayton Hanson, WIS........67.6
08. Danny Morrissey, PSU......63.2
09. Jeff Horner, IA.................62.8
10. David Teague, PUR...........61.3
11. Dee Brown, ILL..................59.2
12. Drew Neitzel, MSU.............59.1
13. Luther Head, ILL................58.6
14. Bracey Wright, IND...........56.3
15. Tony Stockman, OSU.......56.0
16. Adam Haluska, IA.............55.3
17. Robert Vaden, IND............53.8
18. Rico Tucker, MIN...............53.2
19. Vedran Vukusic, NW.........52.3
20. Ronald Coleman, MICH....50.0
20. Jamar Butler, OSU...........50.0


Plea for help:
If anyone knows a quick way to make a table with Blogger that actually lines up, leave a comment or send me an email.

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Poll Update
The new AP and Coaches Polls are out today. Here's how the Big Ten teams fared.

Team..................Rec.....AP...Coaches
Illinois...................9-0........1.........1
Iowa.....................8-1.......16.......21
Michigan State.....5-2.......21.......18
Wisconsin...........5-2.......27.......28
Michigan..............6-3.......NV......33


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Sidebar Update
I expanded the "Erek Hansen Block Watch" to include some other season records that could be in jeopardy. It's over on the right under "Record Chasers."

These paces will no doubt slow down when the games themselves slow down in the Big Ten matchups, but I'll track each stat until the record gets broken or until it's reasonably certain that the player has no chance of getting there.

Notes
- I figured the projections conservatively. I only projected the paces for 31 games (29 regular season, 1 Big Ten tourn. game, 1 post-season game), so the records will be a little more vulnerable if Iowa can make a run at the end of the year. Hansen's projection is for 30 games, since he missed one game to injury.
-Kingsbury's threes made record looks a little safer after Horner's O-fer against Iowa State, but Jeff was right on pace before that.

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Fun With Numbers
Sean Keeler of the Register wrote an article for today's paper in which he analyzed Iowa and Iowa State's use of their possessions during Friday night's game. Knowing how many possessions each team had and how efficiently they used them are excellent tools for understanding a basketball game. I played around with something similar this past week that I got from Dean Oliver - I think it's a little clearer than Keeler's model (which he got from Dean Smith).

Let's get the obvious points out of the way first. Only one team can have possession of the ball at any time. Equally simple, one team's possession ends when the other team obtains the ball and begins their possession. Therefore, if teams alternate control of the ball for the entire game, they will finish the game with an equal number of possessions (give or take one or two because of beginnings and endings of halves). Contrast this with one of Keeler's points -

(a.) It wasn't that the Cyclones didn't have opportunities - they had the ball
more often and did less with it than the Hawkeyes.

Determining possessions in a game (without manually counting them) isn't an exact science, but we can get pretty close if we stop to think about which events cause possession to change between teams. Before going further, I should mention that Oliver treats an offensive rebound as a continuation of a possession since the offense maintains control of the ball, even though it misses its shot.

There are three events that account for the majority of possession changes. The first is any field goal that the offense does not rebound. Possession changes any time the offense scores or the defense rebounds a miss. This term is represented by (FGA - Oreb). Turnovers (TO) are another obvious way to give the other team the ball. The final possibility is a free throw that ends a possession, whether it be a make on the second of a pair or the end of a three-point play, or a miss that gets rebounded. This term is difficult to determine from the box score, but Oliver's years of research led him to believe that about 40% of free throw attempts end a possession (0.4*FTA).

So our formula for possessions becomes -

P = FGA - Oreb + TO + (0.4*FTA)

(This is fairly similar to the stat the Big Ten Wonk ran out the other day, although this uses offensive rebounds and turnovers to fully account for team possessions.)

The first thing the number of possessions in a game tells you is the pace of the game, since, clearly, the more possessions you have, the more often the teams are running up and down the court. So yes, I was a little confused to read -

Smith's method isn't perfect. It doesn't account for the pace of the game. . .

Pace is the exactly what possessions represent. Iowa, the Big Ten's most up-tempo team so far, is averaging 72 possessions a game. Northwestern plays a much slower brand of basketball, and works with 60 poss/game.

Keeler highlights an important point when he covers efficiency. Points/possession (PPP) is a handy stat to compare teams of different styles by measuring how efficiently each team uses its possessions. A team that shoots 60% on 60 possessions might score less than a team that shoots 40% on 80 possessions, but the former team is more likely to win because its opponent will also have only 60 possessions to use, and that opponent will be hard-pressed to score enough to overcome that 60% shooting.

Using straight points/possession usually leaves you with a 1 and several decimals, so it's convenient to multiply it by 100. Now our formula for points per 100 possessions, or offensive efficiency, becomes -

((Pts) / (FGA - Oreb + TO + 0.4*FTA)) * 100

Since the free throw term is not exact, it's best to average each team's possession number to approximate the number of possessions for that game. By that I mean use team A's stats once in the formula, use A's opponent once, and average the two results. Also, you can use a team's points allowed to figure their deffensive efficiency.

Not too difficult, but in just in case you don't think you're up to a little math right now, I went ahead and made a table to summarize the Big Ten teams' offensive and defensive efficiency.

Big Ten Efficiency Ratings (thru 12/13/04)

Team	Poss/Game	Off. Eff.	Def. Eff.
Illinois	67	123	94
Michigan State	71	122	92
Iowa	72	115	99
Ohio State	67	114	90
Penn State	66	108	103
Minnesota	67	106	96
Wisconsin	63	105	95
Michigan	66	100	95
Northwestern	60	99	97
Indiana	64	92	99
Purdue	66	90	102
Average	67	108	97

Again, offensive efficiency is measured as points a team scores per 100 possessions, and defensive efficiency is points allowed per 100 possessions.

Interesting things to note - (1) Michigan State's defense only ranks 8th in points/game allowed, but they're very tough on a more accurate points/possession measure. Also, they're leading Illinois in points per game, but are taking an extra 4 possessions to score the extra points. (2) Iowa's offensive efficiency is comfortably above the conference's average, despite playing one of the most difficult schedules. Of course it would be nice to see that tied-for-third-worst defense improve. (3) Perhaps Ohio State is overlooked by some, as suggested by the Wonk. Their 2 losses were close ones to decent teams (Creighton and Clemson), but none of their 6 wins have been close. And their numbers in that table look pretty nice too.

Well, let me know if you think this kind of stuff is useful or at least interesting, or if I made any major errors along the way. Definitely let me know if it was confusing - my professors have fully informed me that I could use more clarity and organization in my writing. And please note that none of these ideas are mine - if they interest you I suggest taking a look at Oliver's Journal of Basketball Studies.

Since Iowa doesn't play again until Saturday and since I'll be tied up with finals until Friday night, posting this week will probably be limited to tables of Big Ten leaders in some non-traditional stat categories. Stop back and see how you like them.

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Student Ticket Price Drops
Iowa lowered student ticket prices for men's games.............to $20. Ouch. Let's see, $20 gets me -

One Iowa game
Three movies
ESPN College Hoops 2K5
Gas to get home for Christmas
40-ish cans of cheap beer
Baseball Prospectus 2005

$20 for a basketball game still seems really steep to me. Luckily I was one of the 400+ who bought the season package of 18 games for $204 (that's $11.33/game if you don't have the calculator handy).

That article also mentioned the bright yellow shoes Pierre Pierce wore against UNC-Greensboro (I liked 'em):

"I definitely like them," said point guard Jeff Horner, who hasn't broken out
his pair yet. "Hopefully we'll all have them by the time we start the conference
season."

An entire Iowa team outfitted in those shoes? Sweet.

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