2007-08 Villanova Inside Out

I used the past two posts to look at 40 of the most "outside" and "inside" Big East players last season. I applied a 35% playing time as a filter. Three Wildcats showed up on those lists -- Corey Stokes, Dante Cunningham and Antonio Pena -- but what of the other nine scholarship players? Four Wildcats (Malcolm Grant, Andrew Ott, Cas Drummond and Frank Tchuisi) did not log enough time (< 35.0% of the available playing time...) to be considered with the other Big East players while the other five (Corey Fisher, Dwayne Anderson, Scottie Reynolds, Shane Clark and Reggie Redding) were ranked by both Pomeroy and Pelton.

						In/Out		Rank
Player	Min	Min%	FGA	3FGA	FTA	KPom	Pelton	Pelton	KPom
Corey Stokes	642	45.9	202	135	42	-46.0	-14.5	7	5
M. Grant	367	26.2	118	73	44	-24.6	-7.9	NA	NA
Corey Fisher	745	53.2	294	124	79	-15.3	-6.0	30	37
D. Anderson	630	45.0	145	66	43	-15.9	-3.7	37	35
S. Reynolds	1139	81.3	398	206	190	-4.0	-1.4	46	46
Shane Clark	713	50.9	180	56	63	3.9	1.0	58	59
R. Redding	723	51.6	140	36	68	22.9	4.4	74	79
Andrew Ott	16	1.1	4	1	2	25.0	6.3	NA	NA
Cunningham	1050	75.0	261	0	116	44.4	11.0	92	93
Drummond	278	19.9	82	0	36	43.9	12.9	NA	NA
Antonio Pena	678	48.4	183	6	96	49.2	13.3	97	97
F. Tchuisi	13	0.9	6	0	4	66.7	30.8	NA	NA

I have the players listed in "Outside to Inside" order, ie, Corey Stokes heads the list because his shot selection & frequency of free throw suggest his is the greatest tendency to play on the wing/back court. The lower in the list the greater the tendency to play on the inside. Note that Andrew Ott and Malcolm Grant transferred out of the program, to Penn State and Miami (Fla) respectively. Ott will be eligible to play at the end of the fall 2008 semester, while Grant will be eligible to play in the 2009-10 season.

Notes & Observations...
1. The Pelton/Pomeroy rankings for the Villanova players are relatively close, unlike Byron Joynes (see the inside players post), with Scottie Reynolds and Antonio Pena hitting dead-on in both lists. The greatest disparity is Corey Fisher, who is listed seven positions apart in the two lists.
2. That the players always rated as outside (a value <0) or inside (a value > 0) is hardly surprising as both formulas subtract 3 point attempts (3FGAs) from free throw attempts (FTAs). In addition to suggesting where the player takes his field goal attempts, the operation also implies something about the amount of contact the player takes as part of his game. The assumption (true for the most part) is that the more the player "works" inside the paint the more frequent the contact (and subsequent free throw attempts). In Villanova's case there are few surprises; the guards & wings tend to have negative values while power/big forwards and centers tend to have positive numbers. Clark's rating of 1.0 is pretty consistent with his role as an inside players who switches between the #3 and #4 depending on who else is on the court too. Reggie Redding's reluctance to shoot 3s (he took about 25% of his FGAs as 3s) stands in stark contrast to back court mates Scottie Reynolds, Corey Fisher, Corey Stokes and Malcolm Grant who took between 42% (Fisher) and 67% (Stokes) of their FGAs as 3s. While Reggie took very few shots from the outside, his reputation as an overall reluctant shooter was sealed last season as his FGAs declined from November to March, from 5.4 in OOC games to 3.6 in Big East play, down to 1.4 in the postseason.
3. Scottie Reynolds (-1.4 on the Pelton Scale) and Shane Clark (1.0 on the Pelton Scale) are the players operating closest to neutral, showing the smallest bias to play either inside (Clark) or outside (Reynolds). Given that each had a 3 point jumper in his offensive repertoire (), disabuses any notions of either being a reluctant shooter. Clark was less involved in the offense than I anticipated during the preseason however. I believed he would assume a "regular/sharter" type role, taking about 20% or so of the available shots when he was playing. And he did during the OOC part of the schedule. But he seemed to again be slowed by a physical condition once the Big East season was under way. The timing was unfortunate as Drummond was sidelined with a foot injury.

2008-09 Returning Top 25 PPWS Players

Inspired by Ray Floriani's article over on the College Chalktalk Blog, I decided to take a look at those returning Big East players with the best PPWS numbers. PPWS, Points Per Weighted Shot abbreviated, divides the number of points scored by the number of field goal attempts (and a prorated number of free throw attempts). Factoring in free throw points and prorating free throw attempts suggests how efficiently the player posts points when he shoots the ball. While John Gasaway argued that PPWS may be more accurate for identifying inefficient rather than efficient scorers, given enough games I think it can be useful for finding those second and third option scorers in a team's offense. A first option scorer with a high PPWS is quite a player, and is most likely that team's most prolific scorer. I started to post the Top 20 returning players with the highest PPWS (limited to those who played 35% or more of the minutes at their position), but I found 6 tied at 19 last season. So the list includes 25 players...

Player	Coll	PPWS	eFG%	Min%	FTR%	FTM%
John Garcia	SHU	1.27	63.8	51.0	49.3	29.0
Jerry Smith	LVL	1.26	61.5	72.6	25.2	18.5
Ryan Ayers	NDU	1.24	58.1	60.8	38.8	30.0
Hasheem Thabeet	UConn	1.23	56.0	80.3	88.0	63.0
Kyle McAlarney	NDU	1.20	58.3	91.0	14.7	12.4
Wesley Matthews	MU	1.19	52.4	70.5	62.9	49.7
Dante Cunningham	VU	1.19	56.6	74.8	62.8	40.7
Deonta Vaughn	Cin	1.19	55.0	81.9	30.6	25.9
Arinze Onuaku	Cuse	1.18	61.8	78.1	50.0	22.4
Alex Ruoff	WVU	1.17	53.8	78.5	31.2	26.6
Paul Harris	Cuse	1.16	51.1	93.5	72.0	53.8
Kristof Ongenaet	Cuse	1.14	53.4	57.5	48.3	32.8
Will Walker	DPU	1.14	55.2	56.5	18.2	13.0
Austin Freeman	GTU	1.14	54.2	69.2	21.2	16.7
Jerome Dyson	UConn	1.13	50.0	38.8	45.1	37.8
Weyinmi Efejuku	PC	1.13	52.3	56.3	39.3	29.9
Dominique Jones	USF	1.12	51.0	90.9	34.3	27.9
Craig Austrie	UConn	1.12	47.0	58.8	47.0	42.6
Jeremy Hazell	SHU	1.12	52.2	76.0	29.9	23.0
Andre McGee	LVL	1.11	54.2	47.5	21.7	14.5
Sam Young	Pitt	1.11	54.1	82.1	30.5	19.3
Luke Harangody	NDU	1.11	50.3	76.7	44.4	34.1
Gilbert Brown	Pitt	1.11	50.5	62.8	44.9	33.7
Dar Tucker	DPU	1.11	54.6	59.2	28.4	16.5
Dwayne Anderson	VU	1.11	53.7	58.0	31.6	20.0

2007-08 First and second team All-Big East players are highlighted in yellow, while 2007-08 All Big East Rookie Team members are highlighted in lime. [Note... FTR% and FTM% are stats used by Ken Pomeroy to indicate the relationship between free throws and field goals. FTR% is the percentage of free throws to field goal attempts (Pomeroy designates this as FTRate on his Team Scout Report pages) -- useful for determining the frequency by which players get to the free throw line. FTM% divides FTMs by FGAs -- useful for determining the frequency by which players convert their free throw opportunities.]
John Garcia and Jerry Smith provide a useful illustration on how two players can efficiently score through very different ways. Garcia, a center for Seton Hall, hits a high percentage of his (entirely 2 point) field goal attempts and gets to the free throw line pretty consistently (about once for every two FGAs.), even if he is not especially accurate when he gets there (note his FTM% is much lower than his FTA%). An area, which if he could improve, would be very helpful to the Pirates. Jerry Smith, a Lousiville wing guard, by contrast also scores FGAs at a fairly efficient rate (eFG% at 61.5 nearly matches Garcia's), but gets to the line far less frequently. That Luke Harangody (#1), Sam Young (#2), Dominique Jones (#3), Deonta Vaughn (#4), Paul Harris (#7) and Kyle McAlarney (#8) ranked in the Top 10 returning scorers (by points scored, rank shown in parenthesis), while simultaneously ranked in the Top 25 by PPWS, demonstrates that those six combined prolific and efficient scoring as part of their offensive contributions to their respective teams. Those six were clearly first/second scoring options for their teams; we can expect the same again this season.
Other notes & observations:
1. John Garcia would appear to be one of those "players to watch". The often injured front court player for the Seton Hall Pirates not only scored efficiently (see Ken Pomeroy's Seton Hall Scout Report page), but also ranked very highly for offensive and defensive rebounding. The problem it appears (aside from durability) is Garcia's propensity to foul. Garcia picked up 4 or more fouls in 4 of the Pirate's last 5 games last season. Overall he fouled out of 6 of Seton Hall's 32 games. The Pirates generate their offense out of the back court, no doubt giving Garcia the advantage of being often overlooked.
2. Like John Garcia, Ryan Ayers, Wes Matthews, Dante Cunningham, Arinze Onuaku and Alex Ruoff benefit from not being the first or second option on offense. Given the state of their respective teams, their roles (offensively) will most likely remain the same next season.
3. Connecticut HC John Calhoun and much of Husky fandom are looking for a break through season from Hashemm Thabeet. His defensive numbers have been very good (and getting better) over the past two seasons. These offensive numbers suggest he has something to contribute (beyond tap backs...) offensively as well. Thabeet was, as suggested by his Poss% and Shot% (16.1 and 13.1 respectively -- see Pomeroy's Scout Report Page for Connecticut) a role player (4^th or 5^th option on offense) last season. Look for that to change in 2008-09.

Zebras and Cats: Getting Down to Cases

A Gary Parrish column, "Myth Busters", from a few weeks ago put me on to a feature from Robbie Allen's StatSheet.com website. I linked to Robbie's website during the season and have used it for a few of Villanova's stats over time, but I did not realize (until Parrish's column...) that StatSheet.com also collected stats on referees. I have been tracking the fouls, free throws and technicals for villanova's games the past few seasons. I found I could compare referees (or more specifically teams of referees) to each other in how they handled Villanova games. I realized I could now step back to see how these referees handled other games and teams they worked. Was (for example) Tony Greene "harder" or "easier" on Villanova than he was on other teams? The table below provides a few answers and points of comparison.

	All Games			Nova Games
Name	Gm	FPG	STD	Gm	FPG	STD
Hillary, Ted	89	33.8	7.1	1	51.0	NA
Rutecki, Wally	50	37.9	7.1	1	50.0	NA
Laubenstein, Bill	29	37.0	7.0	1	50.0	NA
Perone, Ray	70	37.4	6.7	1	47.0	NA
Hess, Karl	89	36.2	7.1	3	47.0	4.6
Clougherty, Tim	54	34.5	6.2	1	47.0	NA
Donato, Bob	60	37.5	7.1	4	45.0	5.3
Luckie, Jamie	93	38.3	7.1	4	44.0	3.9
Higgins, Tim	69	36.3	7.2	3	43.7	4.2
Kersey, Bryan	102	37.9	7.5	3	43.3	8.1
Stuart, Mike	77	37.1	6.7	2	42.5	4.9
Stephens, Michael	61	38.4	6.5	4	42.3	5.6
Cahill, John	90	35.9	6.1	7	42.0	5.4
Clinton, Bernard	60	40.2	7.5	2	41.5	10.6
Walton, Earl	67	39.1	7.2	2	41.0	5.7
Jones, Les	79	36.7	7.5	1	41.0	NA
Higgins, John	97	35.5	7.2	1	41.0	NA
Collins, J.D.	103	36.8	7.4	2	40.5	2.1
Driscoll, Pat	62	36.8	6.8	2	40.5	3.5
Haney, Jim	68	36.5	7.0	2	40.5	0.7
Greene, Tony	82	35.8	5.7	3	40.3	0.6
O'Connell, Brian	87	35.8	6.3	5	40.2	7.3
Shaw, Curtis	102	39.3	7.3	2	40.0	2.1
Hightower, Ed	80	37.1	8.0	2	40.0	12.7
Prager, Gary	57	36.3	5.7	1	40.0	NA
Burr, Jim	85	35.5	7.7	4	39.5	2.4
Janssen, Paul	56	37.4	7.7	1	39.0	NA
Greenwood, Reggie	65	36.8	6.2	3	38.0	3.0
Shows, Doug	72	36.4	8.1	2	38.0	4.2
Roberts, Mike	61	35.6	6.1	1	38.0	NA
Corbett, Ed	87	34.4	6.5	7	38.0	2.6
Lindsay, Joe	65	35.1	5.9	2	35.5	3.5
Kitts, Mike	87	35.0	7.0	4	35.5	3.7
Clark, Jeff	62	36.8	6.9	3	34.0	3.6

Those looking for stats on Tom O'Neill's Villanova game will be disappointed. The referees listed are those who worked Big East regular season contests -- O'Neill was on the zebra crew that worked the Cats - Golden Knights game in the 1^st round of the Old Spice Classic; O'Neill did not work Big East regular season games. My Villanova game stats however, do include those games against OOC opponents, however, so I am comparing the Villanova games against all games the individual referee worked. In general, the zebra's FPG was higher for the Big East games his crews worked than for all games. I highlighted (in yellow) those referees whose average FPG for Villanova games was outside of the range of the standard deviation for all of their games. In most cases the referee worked a single game, certainly reasonable. Of the three others, two are most likely recognizable to many Wildcat fans, Tim Higgins (recognizable to many basketball fans throughout the country) and Bob Donato, the referee who called Corey Stokes for a blocking foul on Jon Wallace near the end of regulation play in the Villanova-Georgetown game. Yeah, that Bob Donato. The only referee to have a lower average FPG for his Villanova games than he does for all games, Jeff Clark, is highlighted in green. The only referee whose average for Villanova games was lower than his average for Big East games, Doug Shows, is highlighted in brown. Clark worked 3 Villanova games, posting a 3-0 record for those games. Shows worked 2 Villanova games, posting a 1-1 record for those games. Other observations...
1. Ed Corbett and John Cahill worked 7 Villanova games each. That's more than any of the other referees. Villanova games accounted for 7.8% of Cahill's workload, and a straight 8.0% of Corbett's. Villanova's record in Cahill-refereed games was 1-6 last season. For Corbett, the record was 5-2.
2. The FPG for all games and for Villanova games was very close when Mike Kitts worked as part of the crew. At 7.0, Kitt's standard deviation was fairly close to the average for all 34 referees listed (6.9), but at 3.7, his crews showed more consistency when working Villanova games. The Wildcats were 4-0 last season when Kitts was in the crew.
3. Brian O'Connell was the third most prolific Villanova referee, working 5 games (5.75% of his overall workload). Averaging 40.2 FPG, O'Connell seemed to have been, relative to the other 33 referees, more in the "let 'em play" faction. But with a standard deviation of 7.3, O'Connell was pretty inconsistent.

Statistics: Frequency vs Possessions

After I posted Ray's piece I took a look at the SEC Hoops: The Good. The Bad. The Dirty. Blog to see what those guys were up to. They organized data for the entire South East Conference -- teams and players -- and begun to publish a series of "minutes per..." statistics for individual SEC players. For convenience I will call these frequency stats, they suggest "how frequently" a particular player will perform some action (an FGA, an FTA, a foul, a steal, etc.) to distinguish them from per capita (per game) stats -- how frequently will a particular player/team perform a particular act/task over the course of a game, and possession-based stats. Ironically, all are refinements, an end product of some process that begins with the same grist -- the raw data produced by individuals and teams when they play the game. The differences however extend beyond simply the computations and organization, to what they might suggest about a player (or his team) and ultimately to how they can be employed for analysis of personal and team performance.

Consider the compilations of Minutes per Field Goal Attempt Ray produced in his latest Guest Post for the 2008 Villanova squad. The players making up the regular rotation were listed from most frequent shooter (rising sophomore Corey Fisher) to least frequent shooter (rising junior Reggie Redding). I know (using no more than a calculator...or a good estimate) that if he played an entire game Corey Stokes would have taken about 13 FGAs while Reggie Redding would have taken about 8. By comparing Corey Fisher to Scottie Reynolds, I would know that Fish would most likely take the shot (by a small margin...), but that putting both on the floor together would generate a whopping 30.5 FGAs over the course of a 40 minute game. Consider that Villanova averaged (ahhh -- a per capita stat...) 57.5 FGAs per game, the two would have accounted for 53% of the Wildcat's FGAs. What can that tell us about a line up that put both Fish and Reynolds on the floor simultaneously? I have reproduced Ray's table below, but added a third column of the Shot%, a possession-based stat that calculates the probability that a particular player will take the shot.

Player	MpFGA	Shot%
Corey Fisher	2.46	27.5
Malcolm Grant	2.74	22.4
Scottie Reynolds	2.81	24.3
Corey Stokes	2.94	21.9
Casiem Drummond	3.39	20.5
Antonio Pena	3.68	18.8
Dwayne Anderson	4.40	16.0
Shane Clark	4.47	17.6
Dante Cunningham	4.77	17.3
Reggie Redding	5.13	13.3

Shot% and MpFGA do appear to "track", but there is not an exact correlation. If I add Fisher's and Reynold's Shot% I get 51.8, very close to the 40 minute, per capita calculation I did earlier. I can tell by looking at Shot% that a four guard lineup with Drummond in the middle would have been very problematic for the team (the sum of the Shot% for Fisher, Grant, Reynolds, Stokes and Drummond is 116.6 -- the team would have needed two basketballs to maintain everyone's shot rate. The relationship between MpFGA and Shot% is less obvious when I compared the Big East's Top 10 Scorers:

Rnk	Player	School	MpFGA	Shot%
1.	L. Harangody	Notre Dame	1.79	35.6
2.	S. Young	Pittsburgh	2.28	29.0
3.	D. Jones	South Florida	2.63	25.5
4.	B. Laing	Seton Hall	2.72	25.4
5.	D. Vaughn	Cincinnati	2.58	27.6
6.	J. Flynn	Syracuse	2.80	22.7
7.	K. Gransberry	South Florida	2.76	27.3
8.	D. Green	Syracuse	2.36	28.2
9.	D. Burns	DePaul	2.33	29.5
10.	A.J. Price	UConn	2.68	24.4

"Luke Harangody takes a lot of shots" was my first thought when I saw Ray's numbers, putting his MpFGA up against his Shot%, and I have a very good idea of how significant Harangody's role is with respect to Notre Dame's offense. His 35.6% by the way, earned Harangody a #6 ranking overall in D1 according to Pomeroy's Notre Dame Scouting Report. And "Luke Harangody takes a lot of shots relative to his teammates" was my first thought when I saw Harangody's Shot%. Each statistic (MpFGA and Shot%) suggest something about an individual player's tendency to hoist shots (attempt field goals), but MpFGA measures that tendency against time, while Shot% measures it against the other players on the court (at the time the player is shooting...). Which explains why any correlation between an individual's MpFGA and Shot% breaks down with respect to the players in the Top Ten list. How can Jonny Flynn and Kentrall Gransberry, shooting at about the same frequency (2.20 vs 2.76) have a nearly 5% "gap" in their respective Shot% (22.7 vs 27.3)? The answer is the number of possessions (and by implication, shots) the team each plays on typically gets in a game. South Florida had about 67 possessions per game (according to Pomeroy's South Florida Scout Page), while Syracuse (per Pomeroy) had nearly 73.

That each of the Top Ten Scorers should be very efficient offensively is a given (he would not have been allowed to function in the role without demonstrating efficient scoring...), and the table below (same as the one above, just added the player's eFG% & ORtg to the other numbers...):

Rnk	Player	School	MpFGA	Shot%	eFG%	ORtg
1.	L. Harangody	Notre Dame	1.79	35.6	57.1	114.9
2.	S. Young	Pittsburgh	2.28	29.0	54.7	110.1
3.	D. Jones	South Florida	2.63	25.5	53.9	112.2
4.	B. Laing	Seton Hall	2.72	25.4	49.4	106.6
5.	D. Vaughn	Cincinnati	2.58	27.6	55.5	109.5
6.	J. Flynn	Syracuse	2.80	22.7	52.6	112.9
7.	K. Gransberry	South Florida	2.76	27.3	52.8	102.6
8.	D. Green	Syracuse	2.36	28.2	50.5	105.4
9.	D. Burns	DePaul	2.33	29.5	48.6	107.6
10.	A.J. Price	UConn	2.68	24.4	50.8	114.4

Notice that while all of the players listed were indeed efficient shooters (eFG%s nearly 50.0 or better) but all also efficiently generated points for their respective teams (ORtgs > 100). It is no coincidence that all but one of the players who had ORtgs > 110 played on teams that played in a post season tournament (NIT/NCAA). When we look at those same numbers for last season's Wildcat squad what can we learn?

Player	MpFGA	Shot%	eFG%	ORtg
Corey Fisher	2.46	27.5	43.3	94.1
Malcolm Grant	2.74	22.4	53.4	113.2
Scottie Reynolds	2.81	24.3	51.1	105.6
Corey Stokes	2.94	21.9	47.5	101.8
Casiem Drummond	3.39	20.5	52.4	104.4
Antonio Pena	3.68	18.8	46.5	95.4
Dwayne Anderson	4.40	16.0	57.6	114.9
Shane Clark	4.47	17.6	48.3	111.1
Dante Cunningham	4.77	17.3	54.4	104.1
Reggie Redding	5.13	13.3	42.5	99.7

The keys to progress next season may well come down to improvements by the (then sophomores) Fisher, Stokes and Pena. Fisher will need better judgement on when to shoot (and cut back a bit...) and work on setting up teammates. Stokes and Pena will have to become better scorers. Each, along with Drummond, may not get as many opportunities to shoot, but they will have to score more efficiently when they have the ball.

Zebras and Cats: 2007-08 Edition

For a few weeks in February there was a strong chance that referees Jim Burr and Bob Donato, arms outstretched to signal foul and clock stoppage in the (literally) very last seconds of one possession games, would become the icons for Villanova's season. Count on a Sweet Sixteen run to soften the memory, but limping out of the MCI Center saddled with a 14-9 record, 2 of those losses coming complements of quick whistles, it seemed that garnering a fourth consecutive dance bid was slipping just beyond reach. A zebra conspiracy against all things Wildcat? Self-involved fans fixing on excuses for a young and over-hyped team's inability to perform to expectations? Fans who sensed "things were different..." with the referees were not hallucinating fanatics (those are the other teams' supporters...). But there is no statistical evidence (that I have found anyway) that suggests Zebras hate Wildcats. They just called more fouls on them last season. As the chart below suggests, the 'Cats reversed a 2 year trend that had opponents fouling at higher (per game) rates than Villanova. While the disparity was nothing on the scale of the 2004-05 season (also ironically a season that ended in a Sweet Sixteen game...), the balance tipped slightly against the 'Cats.

The very slight average per game advantage to opponents is taken on all games this past season. If the average was less than a single foul per game, the standard deviation suggested the number of fouls per game varied a bit more than expected. For opponents, the standard deviation was 3.39 on an average of 20.1 fouls, while fouls for the 'Cats, the standard deviation was 4.63 on 20.8 fouls, a bit higher than expected, and a little surprising when compared to opponents. Given changes in the starting lineup and rotation throughout the season, combined with the lack of collegiate experience the wide variation in fouls calling is understandable. Ken Pomeroy, per his Villanova Scouting Report calculated Villanova's "experience" at 1.0 years and ranked the Wildcats 324/341, a good indication of the team's relative youth (as a yardstick, consider that Pomeroy calculated the 2007 Connecticut Huskies at 0.5 years and ranked them 335/336).

Breaking the games out by location (home, away, neutral -- see graph below) reveals more detail, and some variations that I did not expect. "Home Cooking" was a myth last season. Villanova held a paltry 0.5 average foul advantage over opponents over the 15 game home slate. This is especially interesting because a team with a home court record like Villanova's last season, 12-3, ought to expect their opponent's foul total to be padded by a few extra change of possession fouls at the end of the game. The average margin of victory, 13.2, may have been large enough to discourage the practice. Despite the record and the modest advantage in average fouls the standard deviation revealed a similar gap in the range of the number of fouls called. Surprisingly, it came to nearly 2.5 fouls. The 'Cats actually did better on neutral courts, holding an advantage of nearly 1 foul (0.9) average over their opponents over the course of 9 games. The gap for the 11 away games explains the fouling disadvantage for Villanova. The Wildcats averaged nearly 3.5 more fouls per game than their opponents. Home Cooking in reverse (Road Curse?) seems appropriate. The Wildcats had a losing road record, 4-7, which may explain some of that difference, if one assumes that the team that is behind fouls 4 - 6 times in the closing minutes as a strategy to force possession changes and give themselves more scoring opportunities. The gap in standard deviations actually favored Villanova by a very modest 0.5 fouls.

Given the relative inexperience of the squad the struggles on the road could have been anticipated. The large inconsistency from game-to-game, even at home is puzzling though. Any comfort to be drawn from these numbers will have to come from the differential in the neutral court games. Villanova was able to hold their average below their opponents, despite the unfamiliar setting. That the neutral court games were played near the beginning of the season (Old Spice Classic) and at the very end of the season (GET and NCAA) should be a good sign for the squad next season.

Methods & terms...
I counted a game as "home" if it was played at the Pavilion or the Wachovia Center, as "away" if it was played on a home court of the opponent and as "neutral" if the game was played at a location not the home court of either team. This season neutral games were played at the Milk House for the Old Spice Classic (though one could argue that the proximity of UCF made the Milk House a virtual home court for the Golden Knights...), Madison Square Garden for the Big East Tournament (though I counted it as "away" for the St. John's game) and the arenas in Tampa and Detroit where the 'Cats played 3 NCAA games.