Well, I think this is it.
I just don't have the time I used to to work up the stats for the games, and the rest of the spine-tingling content that Alan and I once cobbled together isn't writing itself.
We haven't written a season preview in years and most game recaps consist of stats dumps.
I think Woody and Diane described it best:
I'll leave the website live for a while longer in case anyone wants to refer back to the stats from previous years. I may even try to update the links on the sidebar one last time.
I hope someone found this thing useful, and it wasn't a complete waste of time. Alan and I will still be around HoyaTalk and Casual Hoya, as time permits.
Thank you for your support.
Tuesday, November 5, 2013
Friday, March 22, 2013
Recap: Florida Gulf Coast 78, Georgetown 68
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Florida Gulf Coast . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 33 44 77 Points 22 46 68 24 54 78 Effic. 67.4 104.3 88.6 73.6 122.5 101.7 eFG% 35.2 48.6 43.0 34.6 65.2 49.0 TO% 18.4 18.1 18.2 15.3 18.1 16.9 OR% 23.8 27.3 25.6 19.0 21.4 20.0 FTA/FGA 29.6 32.4 31.2 46.2 139.1 89.8 Assist Rate 44.4 73.3 62.5 62.5 76.9 71.4 Block Rate 11.1 12.5 11.8 10.5 5.6 8.1 Steal Rate 6.1 9.1 7.8 6.1 6.8 6.5 2FG% 42.1 50.0 45.9 33.3 56.2 44.1 3FG% 12.5 31.6 25.9 25.0 57.1 40.0 FT% 37.5 83.3 65.0 50.0 75.0 68.2
And so ends another season for your Georgetown Hoyas. I'm not sure if it's because I'm getting older or just I've just grown numb to it, but the Hoyas fifth straight loss to a double-digit seed in the NCAA tournament just doesn't shock or distress me like they used to.
Instead, early in the second half when my sons asked that I play with them rather than watch the game, I said "Sure." Because rolling two five-year-olds into a comforter and tickling them until they could wriggle away was a lot more fun.
A few statistical observations from the evening:
- Georgetown's lack of an interior game came home to roost tonight. In spite of shooting a higher percentage on inside shots than the Eagles [GU: 10-15 on dunks, layups and tips, FGCU: 11-22], the Hoyas simply settled for far too many 2-pt jump shots [GU: 7/22, FGCU 4/12]. When a major conference team is unable to punish a mid-major inside, the playing field is leveled.
- While the game was certainly lost in the vespers half, the Hoyas did manage a stellar opening half of defense and had nothing to show for it but a two-point deficit. Georgetown's offense spurted because - you guessed it - too many jump shots. The Hoyas were 9/27 on FGs in the lift-off half: 3/6 on layups/tips; 5/13 on 2FG jumpers, 1/8 on 3FG jumpers.
- To start the game, the Hoyas grabbed 5/12 own available missed shots on their way to an 18-12 lead, even though they could only generate two points on those offensive rebounds. Georgetown didn't get another offensive rebound until 6:32 was left in the game.
- The bottom fell out during that stunning second-half run by FGCU. The Eagles managed to score on nine consecutive possessions in the course of a 21-5 stretch that broke the game wide open. During that run, the Hoyas attempted only one shot in the paint, which resulted in two (made) free throws for Jabril Trawick.
- Hats off to Markel Starks, who led the team in scoring in each half and was the chief architect of the furious comeback attempt in the last three minutes [10 pts, 2 assist, 1 steal in 2:09 of game time] and to Aaron Bowen, who managed a +8 game in 12 minutes played when Coach Thompson finally called for full-court pressure and a frenetic pace.
- And congratulations to the Florida Gulf Coast Eagles for outplaying the Hoyas all night long in their historic upset win.
more stats after the jump
Wednesday, March 20, 2013
South bracket: log5 prediction
As I did last year and have done in the past, I will be running log5 analysis based on the Pomeroy ratings for at least as long as the Hoyas are in the field.
Ken Pomeroy has run the official log5 odds for the tournament as a whole on his website. Rather than duplicate his what I will try to do here is provide supplementary information.
The first question I want to answer is to what extent the Hoyas were unlucky or lucky to be the #2 seed in the South region as opposed to the #2 seed in some other region. To answer that question, I ran the log5 odds for the Hoyas in each region. Here are the results:
In last year's log5 post, I wondered whether looking at performance solely against teams in the top 100 of the Pomeroy ratings would produce more accurate predictions. Given no team the Hoyas are likely to meet other than Round of 64 opponent FGCU will be ranked outside the top 100, it seemed like measuring how teams fare against tournament-quality teams would be a better predictor of tournament success. To that end, I calculated how every NCAA tournament team fared against the top 100. Please note these are raw averages, not adjusted for opponent or venue. Here is what the South region log5 looks like based on top 100 performance.
As devoted Hoya fans, we are aware the Hoyas experienced of Greg Whittington, which resulted in changes in how the team played. Cognizant of that, I broke down the Hoyas' performance in three ways.
The most natural split would be before and after Whittington's suspension. This is natural for a number of reasons. First, Whittington will not be returning this season. Second, the Hoyas played 11 games against top 100 foes without Whittington, so we do not have a small sample size problem. Third, as Hoya fans, we recognize the current version of the Hoyas is better than the version of the Hoyas we saw in November and December, so we want to think of them at their best.
While I want to adopt this approach, particularly for the third reason, I am not fully comfortable with it for a number of reasons. First, the Hoyas still played those games and 80% of the contributions to those games came from people who will be playing in March. Second, the Hoyas are not the only team whose characteristics have changed over the course of the season. Judging them off post-Whittington ratings reflects a trend-based approach I am otherwise eschewing in my analysis. (If you're curious, Dan Hanner ran those numbers.) Third, they had one particularly anomalous game that is skewing their overall numbers. Half of the effect of removing Whittington comes from removing this one game. Given that I believe college athletics is prone to extreme games resulting from events extrinsic to the on-court (or -field) action, I am in some ways more comfortable with throwing out just one game than I am six.
With that in mind, here's how all three incarnations of the Hoyas fare in the analysis.
I have already discussed the problem of small sample sizes resulting in teams that appear to be much better than their overall body of work indicates. Akron is perhaps the most extreme example of this, but Memphis is another. The Tigers come out slightly better than the Zips, but they did not fare particularly well in their two games against teams ranked in the top 70. I am not comfortable declaring Josh Pastner's squad to be the 16th-best team in the country.
Since this is the second year I have done this, I can also look at last year's results and see which teams overachieved and underachieved relative to how their performance against top 100 teams would lead you to predict. The big overachievers all came from one of the prototypical power conferences. Some of the teams, like Louisville, did fairly well in conference, while others, including Cincinnati, South Florida, and, yes, NC State, fared poorly. Underachievers came from everywhere, but the biggest cause seemed to be mid-majors with gaudy performances against top 100 but not elite foes. Teams in this category included Wichita State, San Diego State, and St. Mary's.
It is worth noting that on the whole Ken's official log5 projection, based on all games, outperformed my raw, unadjusted top 100 performance last year when it came to predicting how the tournament ensued. If this method does not do better this year, I most likely will not be running it next year.
Having gotten my disclaimer out of the way, I wanted to quickly run through projections for the other regions.
Moving on to the other half of the bracket, we see the NCAA's top overall seed is a very good team, but perhaps not quite the strongest team in the field.
I manually adjusted Iona's odds to put them more in line with the other 15 seeds in the field. They played only four games against top 100 competition and went 2-2, beating Denver and Georgia and losing to La Salle and St. Joseph's. Ohio State by this method is by far the weakest #2 seed, coming out just behind Ole Miss, Missouri, and NC State. They still stand a decent chance of getting to the Elite 8, as Arizona and New Mexico are both likely to be overrated by this methodology while the Buckeyes are underrated.
Finally, a gentle reminder: efficiency ratings are not destiny.
Ken Pomeroy has run the official log5 odds for the tournament as a whole on his website. Rather than duplicate his what I will try to do here is provide supplementary information.
Log5 in Other Regions
The first question I want to answer is to what extent the Hoyas were unlucky or lucky to be the #2 seed in the South region as opposed to the #2 seed in some other region. To answer that question, I ran the log5 odds for the Hoyas in each region. Here are the results:
Based on that analysis, the South region is of only average difficulty when it comes to making it to the Sweet 16, but getting to the Elite 8 is a particularly difficult challenge. The reason is the presence of the Florida Gators, the top team in the Pomeroy ratings thanks to their obliteration of the rest of the SEC and some very strong non-conference performances. Should they manage to get past Florida though, they are a slight favorite to advance. Kansas is the weakest #1 seed by the Pomeroy ratings, #4 seed Michigan rates as a tossup, and the Hoyas would be a 60% or greater favorite against any other team from the top half.Bracket 2nd Round Sweet 16 Elite 8 Final 4 South 89.6 60.7 20.0 10.2 East 88.9 63.3 44.5 18.4 Midwest 91.3 55.8 31.4 12.0 West 86.0 57.2 35.1 15.5
Top 100 Performance: South Region
In last year's log5 post, I wondered whether looking at performance solely against teams in the top 100 of the Pomeroy ratings would produce more accurate predictions. Given no team the Hoyas are likely to meet other than Round of 64 opponent FGCU will be ranked outside the top 100, it seemed like measuring how teams fare against tournament-quality teams would be a better predictor of tournament success. To that end, I calculated how every NCAA tournament team fared against the top 100. Please note these are raw averages, not adjusted for opponent or venue. Here is what the South region log5 looks like based on top 100 performance.
Note I listed a couple teams in bold in the bracket. The Hoyas were bolded to highlight their odds. The other teams I bolded are the result of one of the problems inherent in the method, namely that not every team in college basketball plays enough top 100 teams to get a good grasp of how good they are. Like Ohio last year, Akron presents a particular challenge. By virtue of a season sweep of the Bobcats, the 79th-ranked team in the Pomeroy ratings, the Zips come out as a well above-average squad in their seven games against top 100 competition. This seems very implausible to me, so I therefore manually adjusted their rating to reflect average performance against the top 100. This still leaves them an above-average 12th seed and better than North Carolina and Villanova, but does not break the system.Seed Team 2nd Round Sweet 16 Elite 8 Final 4 1 Kansas 98.6 88.4 68.8 36.4 16 Western Kentucky 1.4 0.1 0.0+ 0.0+ 8 North Carolina 55.2 6.8 2.2 0.3 9 Villanova 44.8 4.6 1.3 0.2 5 VCU 63.1 33.6 9.7 2.8 12 Akron 36.9 15.0 3.1 0.6 4 Michigan 77.6 45.0 14.1 4.3 13 South Dakota St. 22.4 6.5 0.9 0.1 6 UCLA 59.7 10.5 4.5 1.2 11 Minnesota 40.3 5.3 1.8 0.4 3 Florida 91.9 81.0 66.5 44.5 14 Northwestern St. 8.1 3.2 1.0 0.2 7 San Diego St. 62.3 25.2 5.4 1.5 10 Oklahoma 37.7 11.4 1.7 0.3 2 Georgetown 91.1 61.9 18.9 7.2 15 FGCU 8.9 1.6 0.1 0.0+
The Effect of Greg Whittington (and Pitt)
As devoted Hoya fans, we are aware the Hoyas experienced of Greg Whittington, which resulted in changes in how the team played. Cognizant of that, I broke down the Hoyas' performance in three ways.
I used the ratings for all games to produce the above odds. Keep in mind that even the least flattering sample size of all games has the Hoyas as an very good team, coming out 11th in the field. While that would not leave them as a #2 seed, they are comparatively much more deserving than last year's #3 seed. They are the best defensive team in the field and have a profile similar to, but better than, last year's #4 seed Louisville team that made it to the Final 4.Sample Off Eff. Def Eff. All Games 96.8 89.4 Post-Whitt 100.7 87.7 W/o Pitt 98.1 87.2
The most natural split would be before and after Whittington's suspension. This is natural for a number of reasons. First, Whittington will not be returning this season. Second, the Hoyas played 11 games against top 100 foes without Whittington, so we do not have a small sample size problem. Third, as Hoya fans, we recognize the current version of the Hoyas is better than the version of the Hoyas we saw in November and December, so we want to think of them at their best.
While I want to adopt this approach, particularly for the third reason, I am not fully comfortable with it for a number of reasons. First, the Hoyas still played those games and 80% of the contributions to those games came from people who will be playing in March. Second, the Hoyas are not the only team whose characteristics have changed over the course of the season. Judging them off post-Whittington ratings reflects a trend-based approach I am otherwise eschewing in my analysis. (If you're curious, Dan Hanner ran those numbers.) Third, they had one particularly anomalous game that is skewing their overall numbers. Half of the effect of removing Whittington comes from removing this one game. Given that I believe college athletics is prone to extreme games resulting from events extrinsic to the on-court (or -field) action, I am in some ways more comfortable with throwing out just one game than I am six.
With that in mind, here's how all three incarnations of the Hoyas fare in the analysis.
In doing this analysis, it's worth noting the Hoyas without Whtitington come out as the fourth-best team in the field. That they still only have a 16.5% chance to make the Final 4 is because Florida and Kansas are the two best teams in the field.Team 2nd Round Sweet 16 Elite 8 Final 4 All Games 91.1 61.9 18.9 7.2 Post-Whitt 94.9 75.3 31.9 16.5 W/o Pitt 93.8 71.0 27.0 12.7
The Problems Inherent in the Method
I have already discussed the problem of small sample sizes resulting in teams that appear to be much better than their overall body of work indicates. Akron is perhaps the most extreme example of this, but Memphis is another. The Tigers come out slightly better than the Zips, but they did not fare particularly well in their two games against teams ranked in the top 70. I am not comfortable declaring Josh Pastner's squad to be the 16th-best team in the country.
Since this is the second year I have done this, I can also look at last year's results and see which teams overachieved and underachieved relative to how their performance against top 100 teams would lead you to predict. The big overachievers all came from one of the prototypical power conferences. Some of the teams, like Louisville, did fairly well in conference, while others, including Cincinnati, South Florida, and, yes, NC State, fared poorly. Underachievers came from everywhere, but the biggest cause seemed to be mid-majors with gaudy performances against top 100 but not elite foes. Teams in this category included Wichita State, San Diego State, and St. Mary's.
It is worth noting that on the whole Ken's official log5 projection, based on all games, outperformed my raw, unadjusted top 100 performance last year when it came to predicting how the tournament ensued. If this method does not do better this year, I most likely will not be running it next year.
Top 100 projection: East region
Having gotten my disclaimer out of the way, I wanted to quickly run through projections for the other regions.
NC State-Temple is one of several intriguing mid-major vs. power conference games. Syracuse, Marquette, and Illinois are all good test cases for whether a stronger adjustment for strength of schedule would help the methodology, as all three performed poorly relative to their seed in strong conferences. Illinois-Colorado is a very interesting matchup in that regard.Seed Team 2nd Round Sweet 16 Elite 8 Final 4 1 Indiana 97.3 69.2 51.5 28.9 16 LIU/James Madison 2.7 0.2 0.0+ 0.0+ 8 NC State 56.7 18.7 10.7 4.2 9 Temple 43.3 12.0 6.1 2.0 5 UNLV 52.9 28.2 9.1 3.0 12 Cal 47.1 23.9 7.1 2.2 4 Syracuse 84.1 45.0 15.3 5.4 13 Montana 15.9 2.9 0.3 0.0+ 6 Butler 53.2 31.1 9.2 3.4 11 Bucknell 46.8 25.9 7.0 2.4 3 Marquette 60.4 28.4 7.5 2.5 14 Davidson 39.6 14.6 2.9 0.7 7 Illinois 40.4 8.5 4.1 1.3 10 Colorado 59.6 16.4 9.4 3.8 2 Miami-FL 93.9 73.9 59.8 40.0 15 Pacific 6.1 1.2 0.3 0.0+
Top 100 projection: Midwest region
Moving on to the other half of the bracket, we see the NCAA's top overall seed is a very good team, but perhaps not quite the strongest team in the field.
Creighton-Cincinnati is perhaps the most interesting game of the round of 64. In addition to the mid-major vs. power conference battle, it also is a contrast of styles, as the Blue Jays are an outstanding offensive team with a weak defense while the Bearcats have a mediocre offense and a strong defense. I have already noted Memphis; my subjective opinion is that this system overrates their chances of a Sweet 16 trip relative to Michigan State's. Yes, St. Louis does seem to be that good, though the official log5 prediction likes Oklahoma State much more than this does. Duke's projection is based on their performance with Ryan Kelly. With Kelly, they come out as the fifth-best team in the field, narrowly behind the Hoyas. In all games, they are the ninth-best team in the field and have a 16.8% chance to make the Final 4.Seed Team 2nd Round Sweet 16 Elite 8 Final 4 1 Louisville 99.0 67.1 42.5 24.5 16 N. Carolina A&T 1.0 0.0+ 0.0+ 0.0+ 8 Colorado St. 55.3 19.4 9.4 4.0 9 Missouri 44.7 13.6 5.9 2.2 5 Oklahoma St. 51.2 19.4 6.4 2.3 12 Oregon 48.8 18.0 5.8 2.0 4 St. Louis 79.3 54.9 28.3 15.2 13 New Mexico St. 20.7 7.7 1.7 0.4 6 Memphis 65.2 36.6 13.6 5.6 11 St. Mary's 34.8 14.5 3.6 1.0 3 Michigan St. 86.7 46.8 16.9 6.7 14 Valparaiso 13.3 2.1 0.2 0.0+ 7 Creighton 72.7 28.5 16.8 7.8 10 Cincinnati 27.3 5.8 2.1 0.5 2 Duke 93.9 64.8 46.6 28.0 15 Albany 6.4 0.9 0.1 0.0+
Top 100 projection: West region
Seed Team 2nd Round Sweet 16 Elite 8 Final 4 1 Gonzaga 99.3 70.7 54.7 39.8 16 Southern 0.7 0.0+ 0.0+ 0.0+ 8 Pitt 54.0 16.6 9.9 5.4 9 Wichita St. 46.0 12.7 7.1 3.6 5 Wisconsin 44.9 23.8 6.6 2.9 12 Ole Miss 55.1 32.0 10.2 5.0 4 Kansas St. 58.6 27.9 7.9 3.5 13 Boise/La Salle 41.4 16.2 3.6 1.3 6 Arizona 63.9 34.7 22.0 9.6 11 Belmont 36.1 14.7 7.3 2.3 3 New Mexico 75.1 42.9 28.1 12.8 14 Harvard 24.9 7.7 3.1 0.7 7 Notre Dame 47.1 20.0 6.5 1.7 10 Iowa St. 52.9 23.8 8.4 2.3 2 Ohio St. 81.4 51.1 23.6 8.9 15 Iona 18.6 5.2 0.9 1.1
I manually adjusted Iona's odds to put them more in line with the other 15 seeds in the field. They played only four games against top 100 competition and went 2-2, beating Denver and Georgia and losing to La Salle and St. Joseph's. Ohio State by this method is by far the weakest #2 seed, coming out just behind Ole Miss, Missouri, and NC State. They still stand a decent chance of getting to the Elite 8, as Arizona and New Mexico are both likely to be overrated by this methodology while the Buckeyes are underrated.
Finally, a gentle reminder: efficiency ratings are not destiny.
Saturday, March 16, 2013
Game stats: Syracuse 58, Georgetown 55 [OT]
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Syracuse . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 28 28 55 Points 20 35 55 29 29 58 Effic. 72.3 100.7 88.2 104.8 83.4 93.0 eFG% 32.7 50.0 41.5 47.8 39.7 43.3 TO% 14.5 20.1 17.6 18.1 25.9 22.5 OR% 30.0 36.8 33.3 26.7 39.1 34.2 FTA/FGA 26.9 48.1 37.7 34.8 37.9 36.5 Assist Rate 75.0 75.0 75.0 88.9 30.0 57.9 Block Rate 0.0 9.1 5.9 5.3 18.8 11.4 Steal Rate 14.5 8.6 11.2 3.6 14.4 9.6 2FG% 36.8 56.2 45.7 41.7 31.8 35.3 3FG% 14.3 27.3 22.2 36.4 42.9 38.9 FT% 42.9 61.5 55.0 87.5 54.5 68.4
And so it goes for the Hoyas in the last year of the "original" Big East tournament.
What I hope doesn't get lost in this is that Mikael Hopkins grades out as clearly the best player on the court for either team tonight (see below). After a mostly difficult season for Hopkins, I was glad to see him have a performance that he can build upon during the NCAA tournament.
more stats after the jump
Thursday, March 14, 2013
Game stats: Georgetown 62, Cincinnati 43
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Cincinnati . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 27 26 53 Points 29 33 62 24 19 43 Effic. 107.0 125.4 116.0 88.5 72.2 80.4 eFG% 52.1 50.0 51.0 55.9 35.7 44.7 TO% 25.8 7.6 16.8 33.2 22.8 28.1 OR% 37.5 35.7 36.7 40.0 20.0 28.0 FTA/FGA 29.2 45.8 37.5 47.1 23.8 34.2 Assist Rate 60.0 54.5 57.1 57.1 57.1 57.1 Block Rate 0.0 7.1 4.8 0.0 25.0 16.1 Steal Rate 14.8 11.4 13.1 14.8 7.6 11.2 2FG% 45.5 45.0 45.2 28.6 42.9 38.1 3FG% 38.5 50.0 41.2 50.0 14.3 35.3 FT% 57.1 81.8 72.2 62.5 80.0 69.2
more stats after the jump
Monday, March 11, 2013
Changes
When performance changes, or someone exceeds or fails to meet expectations, there's always a narrative. It doesn't matter if it is sports, or politics, personnel evaluation or rationalizing why you were late to dinner. The narrative is easier to come up with, always provides an answer, and is simple to communicate. Unlike the truth, which is often hard to get at, not definitive and almost always complicated.
The simple narrative behind Georgetown's improvement has been the rise of Otto Porter in the wake of Greg Whittington's suspension. There's several variations on the narrative here, from innocent to not so innocent:
In all three cases, we'd expect the major cause of improvement of the team to be offensive. In all three cases, we'd expect the shift of possessions away from Greg Whittington to be the key driver of increased offensive ability.
Is it the Offense?
It's the offense.
Given the competition level, the defense may actually have been better, but you'd never expect an offense to improve in raw efficiency in conference play versus a full set that includes seven or eight cupcakes unless it actually, you know, improved. This isn't a quirk of schedule -- the offense did get better (Ed note: I've added the adjusted efficiencies for the two time periods).
Most notably, the team has shot better (#1 in conference play in eFG%) and hit the offensive boards better. (Again, given the competition, they also improved in TOs and FTA, but those are smaller improvements).
Is it the shift of possessions away from Whittington?
One way to find causality in these situations is a Volume, Rate and Assortment calculation. Since we're dealing with per possession efficiency and possessions are our "volume", I've ignored that and focused on a rate and assortment calculation.
An increase in rate means that the player got better between the non-conference and the last ten games. The trade-offs in assortment will show the benefit of moving playing time around.
The results:
The first two columns are non-conference possession usage (including PT and games played) and Offensive Rating. The second two columns are the same, but for the Last Ten Games. The last two columns are the assortment and rate effects on overall efficiency.
Taking a look at the total column, the first takeaway is that the offensive improvement is partially due to a shift in who takes the shots, but equally (if not more) due to improvements in efficiency. Despite the increase in opponents' ability, Porter and Smith-Rivera are playing better than they were in the non-conference on a per possession basis.
There's been a net improvement in who uses the possessions as well. But it's important to note that the grouping of Whittington, Trawick, DSR and Starks nets out at about flat in total affect. Not all of Whittington's possessions went to that group (they are up 16% and Whit is down 18%), so there was some improvement there, but not much.
A more substantial shift was away from Mikael Hopkins as the hub of the offense and towards Otto Porter, and to a lesser extent, Nate Lubick. Porter's non-conference possession numbers are artificially low (he missed 1.5 games), but the shift is unmistakable.
You can still build narratives around this data. Perhaps Otto doesn't take his efficiency to the next level until he realizes he needs to without Greg. Perhaps Thompson never shifts offense from Mikael to Otto (though he was already shifting Mikael's PT and role downward). Perhaps DSR doesn't blossom without the extra playing time. Perhaps Greg's style of playing slowed down the flow of the offense.
Perhaps. Then again, perhaps in the wake of the Pitt loss, Thompson shifts the offense to Otto anyway, Greg plays incredible defense and starts hitting his threes (41% in conference play last year), DSR gets his minutes from Hopkins, not Whittington, and the team goes 16-2 in conference.
Either way, what we do know for certain is that shift of possessions from Greg to DSR, Markel and Trawick had less impact than the shift away from Hopkins or the general improvement of Porter and DSR. The suspension of Whittington still could have been a trigger for either of the two latter causes, but there's little doubt that simply taking the ball away from Whittington was not the cause.
See, I told you the narrative was simpler.
The simple narrative behind Georgetown's improvement has been the rise of Otto Porter in the wake of Greg Whittington's suspension. There's several variations on the narrative here, from innocent to not so innocent:
- Greg's absence made Otto Porter realize he needed to step up
- Greg's suspension made JTIII realize the big lineup was a mistake
- Greg demanded the ball too much for a mediocre offensive player and the Hoyas would be not as good with him.
In all three cases, we'd expect the major cause of improvement of the team to be offensive. In all three cases, we'd expect the shift of possessions away from Greg Whittington to be the key driver of increased offensive ability.
Is it the Offense?
Stat Non-Conf. Last Ten Off Eff. (Adj) 102.2 (102.5) 106.3 (110.5) Def Eff. (Adj) 83.9 (84.1) 91.4 (85.1) eFG% 51.3 53.3 TO% 18.4 20.6 OR% 28.4 33.5 FTA/FGA 34.9 36.5I'm using the last ten even though it's more the last 14 because it's easier (the only other easily available split was con/non-con and that doesn't suit because the first few conference games need to be in the "before" set). I'm using non-conference, because, even though it doesn't include the Pitt debacle, it's directionally right in terms of how the offense was performing.
It's the offense.
Given the competition level, the defense may actually have been better, but you'd never expect an offense to improve in raw efficiency in conference play versus a full set that includes seven or eight cupcakes unless it actually, you know, improved. This isn't a quirk of schedule -- the offense did get better (Ed note: I've added the adjusted efficiencies for the two time periods).
Most notably, the team has shot better (#1 in conference play in eFG%) and hit the offensive boards better. (Again, given the competition, they also improved in TOs and FTA, but those are smaller improvements).
Is it the shift of possessions away from Whittington?
One way to find causality in these situations is a Volume, Rate and Assortment calculation. Since we're dealing with per possession efficiency and possessions are our "volume", I've ignored that and focused on a rate and assortment calculation.
An increase in rate means that the player got better between the non-conference and the last ten games. The trade-offs in assortment will show the benefit of moving playing time around.
The results:
What the heck does that say?Non-conf Last 10 Player Usage ORate Usage ORrate Assort Rate Starks 14% 108.4 20% 98.2 7 (2) Lubick 12% 108.8 14% 100.5 2 (1) Porter 17% 117.0 21% 135.2 5 4 Hopkins 16% 83.7 10% 86.9 (4) 0 Trawick 8% 101.4 11% 99.9 3 (0) DSR 8% 104.6 15% 114.5 8 2 Ayegba 3% 78.7 3% 91.4 0 0 Bowen 1% 73.6 2% 88.0 1 0 Whit 18% 98.4 0% - (18) - Total 96% 97% 3 3
The first two columns are non-conference possession usage (including PT and games played) and Offensive Rating. The second two columns are the same, but for the Last Ten Games. The last two columns are the assortment and rate effects on overall efficiency.
Taking a look at the total column, the first takeaway is that the offensive improvement is partially due to a shift in who takes the shots, but equally (if not more) due to improvements in efficiency. Despite the increase in opponents' ability, Porter and Smith-Rivera are playing better than they were in the non-conference on a per possession basis.
There's been a net improvement in who uses the possessions as well. But it's important to note that the grouping of Whittington, Trawick, DSR and Starks nets out at about flat in total affect. Not all of Whittington's possessions went to that group (they are up 16% and Whit is down 18%), so there was some improvement there, but not much.
A more substantial shift was away from Mikael Hopkins as the hub of the offense and towards Otto Porter, and to a lesser extent, Nate Lubick. Porter's non-conference possession numbers are artificially low (he missed 1.5 games), but the shift is unmistakable.
You can still build narratives around this data. Perhaps Otto doesn't take his efficiency to the next level until he realizes he needs to without Greg. Perhaps Thompson never shifts offense from Mikael to Otto (though he was already shifting Mikael's PT and role downward). Perhaps DSR doesn't blossom without the extra playing time. Perhaps Greg's style of playing slowed down the flow of the offense.
Perhaps. Then again, perhaps in the wake of the Pitt loss, Thompson shifts the offense to Otto anyway, Greg plays incredible defense and starts hitting his threes (41% in conference play last year), DSR gets his minutes from Hopkins, not Whittington, and the team goes 16-2 in conference.
Either way, what we do know for certain is that shift of possessions from Greg to DSR, Markel and Trawick had less impact than the shift away from Hopkins or the general improvement of Porter and DSR. The suspension of Whittington still could have been a trigger for either of the two latter causes, but there's little doubt that simply taking the ball away from Whittington was not the cause.
See, I told you the narrative was simpler.
Saturday, March 9, 2013
Game stats: Georgetown 61, Syracuse 39
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Syracuse . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 27 28 55 Points 25 36 61 18 21 39 Effic. 91.7 130.5 111.2 66.0 76.2 71.1 eFG% 45.7 55.6 51.0 30.0 36.4 33.0 TO% 25.7 7.3 16.4 29.3 21.8 25.5 OR% 35.7 38.5 37.0 38.9 21.4 31.2 FTA/FGA 17.4 29.6 24.0 24.0 31.8 27.7 Assist Rate 75.0 92.3 85.7 28.6 25.0 26.7 Block Rate 10.0 12.5 11.1 9.1 17.6 14.3 Steal Rate 25.7 7.3 16.4 7.3 7.3 7.3 2FG% 27.3 52.9 42.9 30.0 50.0 38.9 3FG% 41.7 40.0 40.9 20.0 0.0 9.1 FT% 100.0 75.0 83.3 50.0 71.4 61.5
more stats after the jump
Wednesday, March 6, 2013
Game stats: Villanova 67, Georgetown 57
TEMPO-FREE BOX SCORE . Visitor Home . GEORGETOWN Villanova . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 33 32 65 Points 29 28 57 33 34 67 Effic. 87.2 88.2 87.7 99.2 107.1 103.1 eFG% 56.5 50.0 53.0 54.8 53.8 54.4 TO% 36.1 34.7 35.4 27.1 25.2 26.1 OR% 38.5 41.2 40.0 33.3 30.0 32.0 FTA/FGA 21.7 11.1 16.0 76.2 200.0 123.5 Assist Rate 83.3 72.7 78.3 70.0 33.3 56.2 Block Rate 0.0 30.0 12.5 0.0 0.0 0.0 Steal Rate 15.0 9.5 12.3 24.1 22.1 23.1 2FG% 62.5 37.5 50.0 50.0 40.0 45.8 3FG% 28.6 45.5 38.9 42.9 66.7 50.0 FT% 60.0 33.3 50.0 62.5 76.9 71.4
more stats after the jump
Saturday, March 2, 2013
Game stats: Georgetown 64, Rutgers 51
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Rutgers . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 29 34 62 Points 29 35 64 28 23 51 Effic. 100.6 104.2 102.5 97.1 68.5 81.7 eFG% 53.6 50.0 51.5 50.0 33.9 41.7 TO% 24.3 17.9 20.8 24.3 17.9 20.8 OR% 20.0 35.7 29.2 37.5 18.2 26.3 FTA/FGA 19.2 35.7 27.8 Assist Rate 100.0 22.2 53.3 72.7 44.4 60.0 Block Rate 5.6 22.7 15.0 14.3 0.0 4.5 Steal Rate 6.9 8.9 8.0 6.9 14.9 11.2 2FG% 42.9 53.3 50.0 38.9 36.4 37.5 3FG% 42.9 25.0 36.4 50.0 16.7 35.7 FT% 73.7 69.6 71.4 40.0 40.0 40.0
more stats after the jump
Wednesday, February 27, 2013
Game stats: Georgetown 79, UConn 78 [2OT]
TEMPO-FREE BOX SCORE . Visitor Home . GEORGETOWN UCONN . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 32 29 61 Points 19 60 79 22 56 78 Effic. 59.2 138.0 104.4 68.6 128.8 103.1 eFG% 31.0 72.2 57.0 36.5 60.0 50.8 TO% 28.1 13.8 19.8 28.1 20.7 23.8 OR% 12.5 25.0 18.8 25.0 44.4 35.3 FTA/FGA 38.1 30.6 33.3 15.4 25.0 21.2 Assist Rate 66.7 57.1 59.3 66.7 68.4 67.9 Block Rate 4.8 12.5 8.1 18.8 23.8 21.6 Steal Rate 12.5 9.2 10.6 9.4 6.9 7.9 2FG% 31.2 52.4 43.2 38.1 56.2 45.9 3FG% 20.0 66.7 55.0 20.0 41.7 37.9 FT% 75.0 72.7 73.7 75.0 80.0 78.6
more stats after the jump
Tuesday, February 26, 2013
The Inevitable Comp
Both teams have sharpshooting junior point guards and Onions at the SG position.
Both teams have a junior glue guy at PF. Both teams have a freshman scorer forcing his way into PT.
Both teams lost two early games in the Big East (including one to Pitt), then proceeded to go on a run.
The 2006-07 team went out after their Pitt loss and went 12-1 the rest of the way. The current team is 11-1 since their Pitt loss.
The 2006-07 opponents' average Pomeroy ranking during the run was 63. The current team's is 74 -- though if you just take the current 9 game winning streak, it is 64.
The average margin of victory during the 12-1 run was 10.5. The average margin of victory during this season's 11-1 run is 10.8.
Do with this as you will.
Saturday, February 23, 2013
Game stats: Georgetown 57, Syracuse 46
TEMPO-FREE BOX SCORE . Visitor Home . GEORGETOWN Syracuse . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 32 27 59 Points 21 36 57 23 23 46 Effic. 65.1 133.1 96.3 71.3 85.0 77.7 eFG% 26.7 60.4 41.7 36.2 40.5 38.0 TO% 15.5 25.9 20.3 24.8 29.6 27.0 OR% 21.7 58.3 34.3 30.0 46.7 37.1 FTA/FGA 20.0 33.3 25.9 10.3 42.9 24.0 Assist Rate 57.1 58.3 57.9 55.6 37.5 47.1 Block Rate 7.1 6.2 6.7 35.3 8.3 24.1 Steal Rate 15.5 14.8 15.2 9.3 14.8 11.8 2FG% 29.4 58.3 41.4 42.9 43.8 43.3 3FG% 15.4 41.7 28.0 20.0 20.0 20.0 FT% 83.3 87.5 85.7 66.7 66.7 66.7
I guess Alan's post about Otto was spot-on. Now I'm expecting a post from him before every game the rest of the way.
And from the category of I'm not saying, but I'm saying:
- In 16 minutes of game action with Mikael Hopkins on the court, the Hoyas were outscored by Syracuse 20-11.
- In 27 minutes of game action with Moses Ayegba on the court, the Hoyas outscored Syracuse 51-26.
more stats after the jump
Friday, February 22, 2013
Ottomatic
Player ORating Usage % DRating Net Points Porter '12-13 117 25% 80 +13 Freeman '10-11 109 26% 100 +5 Freeman '09-10 127 21% - +5 Monroe '09-10 106 26% - +9 Hibbert '07-08 120 26% - -Unfortunately, I can't get good conference stats on Jeff Green's 06-07 run, but Porter is likely having the best in conference season for a Hoya since Roy Hibbert's dominant 2007-08.
His offensive game has been wildly efficient despite increasing his shot attempts. But Otto doesn't nearly get enough credit for being the lynchpin of a defense that has not really taken a step back since Greg Whittington's suspension.
Wednesday, February 20, 2013
Game stats: Georgetown 90, DePaul 66
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN DePaul . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 33 36 69 Points 43 47 90 29 37 66 Effic. 129.3 130.3 129.7 87.2 102.5 95.1 eFG% 69.2 75.0 72.1 38.7 42.6 40.8 TO% 9.0 22.2 15.9 27.1 13.9 20.2 OR% 16.7 30.0 22.7 50.0 34.8 42.2 FTA/FGA 46.2 34.6 40.4 29.0 32.4 30.8 Assist Rate 56.2 76.5 66.7 63.6 28.6 44.0 Block Rate 9.1 18.5 14.3 10.5 5.6 8.1 Steal Rate 21.1 8.3 14.4 3.0 11.1 7.2 2FG% 63.2 66.7 64.9 40.9 48.1 44.9 3FG% 57.1 62.5 60.0 22.2 14.3 18.8 FT% 58.3 88.9 71.4 55.6 72.7 65.0
more stats after the jump
Sunday, February 17, 2013
By special request: Lineup stats
While it has been very quiet around these parts for the past few months, I was prodded by someone over at Hoyatalk to dust off the old Cray II computer in my mom's basement and run some lineup stats for your beloved Georgetown Hoyas.
Now this is a bit of a tricky task this year, since the loss of Greg Whittington has forced the team to re-make itself on the fly, to surprisingly good results.
So, in light of the new WithOut Whittington era now upon us, I decided to break out the season into two parts, much like OverTheHilltop did over at Casual Hoya. One difference, though: instead of looking at all thirteen games played with Greg, I decided to pare the list down to the nine games either against top-150 competition or where the game remained competitive into the second half.
WithWhit WoW Duquesne St. John's (a) UCLA Providence Indiana S. Florida Tennessee Notre Dame Texas Louisville Towson Seton Hall W. Carolina St. John's (h) Marquette (a) Rutgers (a) Pittsburgh Marquette (h) . Cincinnati
First, let's take a look at lineup minutes by position, using the same rules we've used before - players are sorted by height, shortest to tallest, with weight serving as the tie-breaker (one exception here: even though Whittington is bigger than Porter, I slot Otto in the bigger position between the two). All heights and weights come from the GU website. Not all slots will add up to 40 minutes, both due to rounding and because I don't show any player with less than a minute played at a position.
WithWhit:
- Starks [31], Smith-Rivera [9]
- Trawick [19], Whittington [9], Smith-Rivera [9], Domingo [2]
- Whittington [24], Porter [11], Trawick [4], Domingo [2]
- Porter [23], Lubick [14], Whittington [3]
- Hopkins [22], Lubick [14], Ayegba [4]
WoW:
- Starks [37], Smith-Rivera [3]
- Smith-Rivera [26], Trawick [13], Caprio [1]
- Trawick [15], Porter [15], Bowen [9]
- Porter [19], Lubick [18], Bowen [1], Hopkins [1]
- Hopkins [20], Lubick [11], Ayegba [9]
Jabril Trawick has picked up about 10 of those available minutes per game, playing as a slightly undersized "3" in conference play, depending upon match-up. Also, Aaron Bowen has stepped into the lineup for about nine minutes a game (skipping over Stephen Domingo, who struggled in the OOC).
But this also causes a bit of a cascade effect, as DSR is now playing the major role as the shooting guard on the team (with some help from Trawick), meaning that Markel Starks has to play nearly the entirety of every game as the point guard.
Otto Porter is also playing a few more minutes as the small forward (i.e. his position in the starting lineup), but this is really from the effect of playing Moses Ayegba a bit more each game, letting Nate Lubick spend less time as an undersized center.
After the jump, lineup efficiencies.
Saturday, February 16, 2013
Game stats: Georgetown 62, Cincinnati 55
TEMPO-FREE BOX SCORE . Visitor Home . GEORGETOWN Cincinnati . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 35 30 66 Points 31 31 62 25 30 55 Effic. 87.8 103.1 94.6 70.8 99.8 83.9 eFG% 48.1 40.9 44.8 32.7 37.5 35.2 TO% 17.0 13.3 15.3 14.2 20.0 16.8 OR% 7.1 26.7 17.2 19.0 47.4 32.5 FTA/FGA 34.6 72.7 52.1 65.4 46.4 55.6 Assist Rate 58.3 50.0 55.0 50.0 66.7 58.8 Block Rate 0.0 6.7 3.3 23.5 21.1 22.2 Steal Rate 8.5 6.7 7.6 8.5 6.7 7.6 2FG% 64.7 31.6 47.2 46.7 40.0 43.3 3FG% 11.1 66.7 25.0 9.1 23.1 16.7 FT% 66.7 81.2 76.0 47.1 69.2 56.7
Some quick thoughts:
- Cincinnati grabbed a ton of offensive rebounds in the second half [9 of 19 available misses], but was actually under it's conference average [35%] overall for the game.
- Porter, Starks, Lubick and Smith-Rivera were all better than +10 in plus/minus for the game, but only one other Hoya (Ayegba) had a positive plus/minus.
- This was the third consecutive game, and fourth in five, where Otto Porter was in double-digits in Net Points. I wonder if anyone outside of the Hoya-sphere realized just how good he is.
- In spite of the winning streak and the out-right lead in the Big East standings as of this morning, I still have the Hoyas rated as the fourth best team in the conference. Delusion, meet cynicism.
Monday, February 11, 2013
Game stats: Georgetown 63, Marquette 55
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Marquette . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 32 32 64 Points 33 30 63 23 32 55 Effic. 102.8 94.8 98.7 71.6 101.1 86.2 eFG% 48.2 39.6 44.2 50.0 46.3 47.8 TO% 24.9 19.0 21.9 37.4 22.1 29.8 OR% 43.8 37.5 40.6 18.2 41.2 32.1 FTA/FGA 25.0 70.8 46.2 42.1 33.3 37.0 Assist Rate 63.6 77.8 70.0 55.6 45.5 50.0 Block Rate 7.7 15.0 12.1 6.7 25.0 16.1 Steal Rate 15.6 9.5 12.5 9.3 9.5 9.4 2FG% 40.0 50.0 45.2 61.5 40.0 48.5 3FG% 38.5 12.5 28.6 16.7 42.9 30.8 FT% 85.7 64.7 70.8 50.0 77.8 64.7
more stats after the jump
Saturday, February 9, 2013
Game stats: Georgetown 69, Rutgers 63
TEMPO-FREE BOX SCORE . Visitor Home . GEORGETOWN Rutgers . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 28 26 54 Points 33 36 69 33 30 63 Effic. 117.7 137.2 126.8 117.7 114.3 115.7 eFG% 64.6 63.0 63.8 61.9 36.4 46.3 TO% 25.0 15.2 20.2 10.7 19.1 14.7 OR% 36.4 55.6 45.0 16.7 60.0 45.9 FTA/FGA 20.8 34.8 27.7 47.6 24.2 33.3 Assist Rate 53.8 64.3 59.3 63.6 50.0 57.1 Block Rate 0.0 13.6 8.3 0.0 0.0 0.0 Steal Rate 3.6 11.4 7.3 17.8 3.8 11.0 2FG% 57.1 65.0 61.8 50.0 27.3 36.1 3FG% 50.0 33.3 46.2 57.1 36.4 44.4 FT% 40.0 87.5 69.2 70.0 75.0 72.2
The biggest perceived negative from today's game is likely the huge advantage in offensive rebounds [17-9] that Rutgers had over the Hoyas.
The reality is that the teams got their own misses at essentially the same rate [GU: 45%, RU: 46%], it's just that Rutgers missed a ton more shots.
more stats after the jump
Saturday, February 2, 2013
Game stats: Georgetown 68, St. John's 56
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN St. John's . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 30 29 60 Points 39 29 68 28 28 56 Effic. 129.8 98.9 114.1 93.2 95.5 94.0 eFG% 67.2 40.0 53.4 38.5 32.4 34.9 TO% 26.6 13.6 20.1 16.6 13.6 15.1 OR% 66.7 38.1 48.5 27.8 50.0 40.5 FTA/FGA 0.0 23.3 11.9 46.2 13.5 27.0 Assist Rate 58.8 60.0 59.3 44.4 45.5 45.0 Block Rate 11.1 18.5 15.6 13.3 11.1 12.1 Steal Rate 6.7 10.2 8.4 6.7 10.2 8.4 2FG% 80.0 33.3 54.5 38.9 33.3 35.6 3FG% 35.7 33.3 34.6 25.0 20.0 22.2 FT% - 71.4 71.4 66.7 80.0 70.6
more stats after the jump
Wednesday, January 30, 2013
Game stats: Georgetown 74, Seton Hall 52
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Seton Hall . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 32 36 69 Points 32 42 74 22 30 52 Effic. 98.8 115.1 107.5 67.9 82.2 75.5 eFG% 50.0 59.6 54.9 42.9 34.1 38.4 TO% 24.7 16.4 20.3 30.2 36.3 OR% 35.7 37.5 36.7 35.7 36.8 36.4 FTA/FGA 40.0 84.6 62.7 23.8 86.4 55.8 Assist Rate 81.8 76.9 79.2 50.0 66.7 57.1 Block Rate 10.0 15.4 13.0 6.7 5.9 6.2 Steal Rate 27.8 16.4 21.8 21.6 8.2 14.5 2FG% 53.3 47.1 50.0 60.0 23.1 39.1 3FG% 30.0 55.6 42.1 18.2 33.3 25.0 FT% 70.0 50.0 56.2 80.0 78.9 79.2
Notes:
- Georgetown out-scored Seton Hall 50-16 when Otto Porter was in the game.
- Seton Hall turned the ball over on 26 of their first 53 possessions (the official scorer missed one).
- In 10:28 of game time in conference play, Stephen Domingo has yet to attempt a shot (including FTs).
Saturday, January 26, 2013
Game stats: Georgetown 53, Louisville 51
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Louisville . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 32 29 60 Points 33 20 53 29 22 51 Effic. 103.7 70.0 87.8 91.1 77.0 84.5 eFG% 56.2 32.0 43.9 37.0 39.1 38.0 TO% 28.3 28.0 28.2 25.1 17.5 21.5 OR% 33.3 36.8 35.5 26.7 29.4 28.1 FTA/FGA 25.0 24.0 24.5 52.2 43.5 47.8 Assist Rate 63.6 12.5 42.1 37.5 50.0 43.8 Block Rate 10.0 0.0 5.7 9.1 4.8 6.2 Steal Rate 6.3 10.5 8.3 18.9 14.0 16.6 2FG% 54.5 38.1 43.8 35.0 40.0 37.1 3FG% 38.5 0.0 29.4 33.3 25.0 27.3 FT% 100.0 66.7 83.3 100.0 40.0 72.7
more stats after the jump
Monday, January 21, 2013
Game stats: Georgetown 63, Notre Dame 47
TEMPO-FREE BOX SCORE . Visitor Home . Georgetown Notre Dame . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 28 27 55 Points 34 29 63 21 26 47 Effic. 120.9 107.1 113.9 74.7 96.0 84.9 eFG% 65.9 56.5 61.1 32.5 39.7 36.7 TO% 10.7 22.1 16.3 14.2 3.7 9.0 OR% 10.0 33.3 22.7 11.8 25.0 18.9 FTA/FGA 36.4 17.4 26.7 65.0 17.2 36.7 Assist Rate 76.9 81.8 79.2 50.0 72.7 64.7 Block Rate 13.3 5.6 9.1 0.0 13.3 6.7 Steal Rate 3.6 3.7 3.6 3.6 18.5 10.8 2FG% 66.7 46.7 56.7 33.3 55.6 45.5 3FG% 42.9 50.0 46.7 20.0 9.1 12.5 FT% 62.5 75.0 66.7 61.5 60.0 61.1
It's going to be one of those years, isn't it?
More stats after the jump
Sunday, January 20, 2013
Game stats: South Florida 61, Georgetown 58
TEMPO-FREE BOX SCORE . Visitor Home . GEORGETOWN USF . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 27 28 55 Points 31 27 58 23 38 61 Effic. 115.0 98.0 106.4 85.3 111.9 eFG% 72.5 44.0 56.7 32.8 65.9 47.1 TO% 29.7 25.4 27.5 11.1 7.3 9.2 OR% 37.5 43.8 41.7 33.3 16.7 27.3 FTA/FGA 15.0 28.0 22.2 20.7 45.5 31.4 Assist Rate 75.0 60.0 68.2 44.4 72.7 60.0 Block Rate 0.0 0.0 0.0 0.0 11.8 7.4 Steal Rate 7.4 3.6 5.5 22.3 7.3 14.7 2FG% 70.0 47.1 55.6 53.3 44.4 50.0 3FG% 50.0 25.0 38.9 7.1 53.8 29.6 FT% 66.7 71.4 70.0 66.7 90.0 81.2
So what happened in the second half? It was a combination of the Hoyas inability to turn the Bulls over along with watching South Florida make 7 of 13 attempts from behind the arc. In other words, it was like almost every NCAA tournament game since 2008.
It's also worth noting that the Bulls didn't make a high percentage of shots from behind the arc for the game as a whole. The real culprit on defense - and the main reason the Hoyas lost - was that Georgetown simply couldn't generate turnovers.
more stats after the jump
Wednesday, January 16, 2013
Game stats: Georgetown 74, Providence 65
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Providence . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 29 38 67 Points 38 36 74 19 46 65 Effic. 129.9 94.3 109.9 65.0 120.4 96.5 eFG% 75.0 41.3 58.5 29.3 58.6 44.0 TO% 13.7 23.6 19.3 23.9 23.6 23.8 OR% 0.0 27.8 18.5 43.5 43.8 43.6 FTA/FGA 12.5 95.7 53.2 20.7 51.7 36.2 Assist Rate 68.8 66.7 68.0 75.0 57.1 63.6 Block Rate 9.5 11.8 10.5 12.5 10.5 11.4 Steal Rate 17.1 15.7 16.3 6.8 15.7 11.9 2FG% 75.0 42.1 57.1 33.3 47.1 39.5 3FG% 50.0 25.0 41.7 12.5 50.0 35.0 FT% 66.7 77.3 76.0 33.3 80.0 66.7
more stats after the jump
Saturday, January 12, 2013
Game stats: Georgetown 67, St. John's 51
TEMPO-FREE BOX SCORE . Visitor Home . GEORGETOWN St. John's . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 34 38 72 Points 36 31 67 19 32 51 Effic. 107.1 80.8 92.9 56.5 83.4 70.7 eFG% 53.4 38.5 46.4 34.8 40.3 38.1 TO% 14.9 28.7 22.2 20.8 10.4 15.3 OR% 27.8 30.0 28.9 5.3 20.8 14.0 FTA/FGA 27.6 57.7 41.8 43.5 16.7 27.1 Assist Rate 78.6 40.0 62.5 37.5 38.5 38.1 Block Rate 5.6 16.0 11.6 28.0 4.5 17.0 Steal Rate 11.9 5.2 8.3 11.9 5.2 8.3 2FG% 44.0 45.5 44.7 44.4 40.0 41.9 3FG% 75.0 0.0 37.5 0.0 27.3 18.8 FT% 62.5 73.3 69.6 30.0 50.0 37.5
more stats after the jump
Wednesday, January 9, 2013
Game stats: Pittsburgh 73, Georgetown 45
TEMPO-FREE BOX SCORE . Home Visitor . GEORGETOWN Pittsburgh . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 29 30 60 Points 22 23 45 37 36 73 Effic. 75.4 75.8 75.6 126.7 118.6 122.6 eFG% 40.6 35.7 37.8 65.4 54.3 60.2 TO% 30.8 26.4 28.6 20.6 19.8 20.2 OR% 16.7 41.2 31.0 41.7 41.7 41.7 FTA/FGA 75.0 66.7 70.3 19.2 65.2 40.8 Assist Rate 50.0 28.6 38.5 73.3 41.7 59.3 Block Rate 14.3 10.0 12.2 33.3 0.0 16.0 Steal Rate 6.9 0.0 3.4 10.3 9.9 10.1 2FG% 41.7 46.2 44.0 52.4 55.0 53.7 3FG% 25.0 12.5 16.7 80.0 33.3 62.5 FT% 75.0 57.1 65.4 60.0 73.3 70.0
A simple question to ask after the game is whether that was the worst loss by the Hoyas since John Thompson III arrived. It was.
By our measure, last night Georgetown played about as well as a team ranked 250th by Ken Pomeroy (that's equivalent to Texas Tech or Cornell right now). The Hoyas previous worst effort was in Coach Thompson's very first game as a coach, an 18-point loss at home against Temple.
more stats after the jump
Saturday, January 5, 2013
Game stats: Marquette 49, Georgetown 48
TEMPO-FREE BOX SCORE . Visitor Home . Georgetown Marquette . 1st Half 2nd Half Total 1st Half 2nd Half Total Pace 27 29 55 Points 19 29 48 20 29 49 Effic. 71.2 101.7 87.0 75.0 101.7 88.8 eFG% 36.5 55.0 44.6 31.5 55.9 40.9 TO% 15.0 21.0 18.1 18.7 21.0 19.9 OR% 21.1 15.4 18.8 38.1 12.5 31.0 FTA/FGA 11.5 45.0 26.1 14.8 82.4 40.9 Assist Rate 88.9 66.7 77.8 85.7 88.9 87.5 Block Rate 6.2 18.2 11.1 4.8 20.0 9.7 Steal Rate 15.0 14.0 14.5 3.7 14.0 9.1 2FG% 38.1 50.0 41.9 25.0 72.7 44.4 3FG% 20.0 40.0 33.3 27.3 16.7 23.5 FT% 0.0 77.8 58.3 75.0 71.4 72.2
more stats after the jump
Thursday, January 3, 2013
Josh Smith
Smith's struggles at UCLA have been well-documented, as his weight has kept him off the floor and ineffective when on it. There's little doubt that if Smith cannot get in shape, he will not get significant playing time. Thompson and Howland do not seem very similar in their public personas, but neither has much playing time for players who do not play defense.
However, assuming Smith can get in shape, what kind of player will he be? In his high school days, Smith was known for his touch, soft hands, ability to use his size and surprising quickness. The immediate comparison that comes to mind for most Hoya fans is Mike Sweetney (unfortunately, right down to potentially eating their way out of an NBA career).
Would Mike -- who was a good passer but never racked up assist numbers like the Hoyas' centers usually do -- be as effective distributing the ball? How efficiently awesome would a player like Mike be in an offense that was designed to get him better shots than "here, score against a triple team!"? How many cutters would get easy layups when those triple teams came?
How comparable is Josh Smith to Mike Sweetney? Smith is actually listed as two inches taller, but he looks a bit softer than college Sweetney -- not just in weight but also in pure strength. Still, they seem very comparable subjectively. Objectively, these are their freshman years (Smith stats from kenpom, Sweetney from this post):
Player Min% Poss% Shot% TS% O/D Reb% ARate TORate Blk% Stl% ORtg Sweetney 60% 24% 26% 55% 13/17% 11% 22% 3% 2% 107 Smith 52% 26% 25% 58% 20/14% 7% 16% 5% 2% 110
Darn similar. Smith was an efficient scorer from the go, took a decent amount of shots and was a beast of an offensive rebounder.
This isn't to say (an in shape) junior Josh Smith will be similar to junior Mike Sweetney -- who took a ton of shots, made most of them and was an all-around beast. But Smith was probably the better offensive player as a freshman, and if it really is just the weight holding him back, even his freshman self would be a tremendous add to this team.
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