FanPost

UCLA v. Villanova: By The Numbers

Bumped. GO BRUINS. -N

Well, didn't last night's finish take a few months off our our lives. After squeaking out a win in a game reminiscent of the second-round battles of recent years, our Bruins now face the real second-round game, against a Villanova wildcats team playing on its second home court.

Villanova enters Saturday's game with a 27-7 record, earning an at-large berth and the East region's #3 seed after finishing 4th in the Big East. In the Big East Tournament, the Wildcats edged out a one point victory against Marquette before losing to Louisville in the Semifinal round, 69-55. The Wildcats played a fairly difficult version of the unbalanced Big East schedule, with home-and-home matchups with Syracuse, Marquette, and Providence; in conference, they played 10 games against NCAA tournament teams, 12 games against tournament competitors overall, with a 6-6 record (Entering the week, UCLA had played 14 games against NCAA tournament competitors, with a 7-7 record).

Villanova and UCLA have faced two three common opponents: Texas, DePaul and Notre Dame, with similar results. Villanova lost to Texas, 67-58 in New York on December 9, won at DePaul, 74-72, and defeated Notre Dame in their penultimate regular season game, 77-60 in South Bend. The Bruins lost to Texas in Austin by a 68-64 score, beat DePaul 72-54 in the Wooden Classic , and beat Notre Dame, 89-63 in Pauley. [edit to include DePaul, HT to godblesstyus]

Now to the numbers...

RPI UCLA: #33, Villanova: #13

Pomeroy UCLA: #10, Villanova: #19

Sagarin UCLA: #17, Villanova: #16

The variance among the different computer ratings in relation to the treatment of UCLA is certainly notable here. While the RPI of the Bruins compared to Villanova reinforces the seeding advantage held by the Wildcats, the other, more predictive rating systems see a much closer matchup.

One quick note about the three rating systems which I cite here. Each of the ratings uses a different methodology, and a different way of measuring success in order to come up with a ranking. The RPI uses a formula based upon the win % of each team, plus the win % of each of its opposing teams, and the win % of the opponent's opponents. This formula does not look at how each team earns its record (does not look at score margin, turnovers or anything other than Wins and Losses). Ken Pomeroy's formula, rather than focusing on only W-L, uses a complex set of game data which aims to predict future performance, rather than accounting for how good or bad a team has performed to date. The Sagarin ranking that I post here is actually a synthesis of two separate ratings which Sagarin calculates; one being an RPI-like formula taking into account only wins and losses, the other taking into score margin rather than a team's record.

Sagarin considers the latter of his formulas to be the better predictor of future performance. To illustrate the different results which arise, and to give us additonal points of reference going into tomorrow afternoon, here are the the ratings broken down by formula.

Sagarin Won-Loss: UCLA: #21, Villanova: #10

Sagarin Predictive: UCLA: #10, Villanova: #19

While the Sagarin Predictive formula, from what I can gather, is functionally quite different from Pomeroy, they both come up with the same relative ranking of these teams, and both find UCLA to be the better team, though by a slight margin.

Next is a look at the efficiency metrics and pace of play for the Bruins and Villanova.

Villanova

* Offense: 114.3 points/100 possessions (#24 in D-1)

* Defense: 91.4 points/100 possessions (#27 in D-1)

* Pace: 68.8 possessions/40 minutes (#74 in D-1)

UCLA

* Offense: 120.6 points/100 possessions (#3 in D-1)

* Defense: 92.8 points/100 possessions (#43 in D-1)

* Pace: 66.4 possessions/40 minutes (#163 in D-1)

As rye noted in his post on Villanova, the Wildcats play at a noticeably faster pace than do the Bruins, gaining about two and a half additional possessions per game. Their success has also been well balances among both sides of the court.

Using the above efficiency measures, together with the average pace of the two teams' play, the average game score of each team, based upon an equal schedule composed of average Division 1 teams would be:

Villanova: 79-63

UCLA: 80-62

By all measures, tomorrow's game looks to be a tight battle; no matter who comes out on top, I would expect the result to come down to the final possession or two, where I hope that a repeat of last night's last second defensive lockdown will allow our Bruins to spend a few extra days on the east coast and a trip up I-95 to Boston.

Pomeroy projects the Bruins to win a nail-biter, 74-73, accounting for Villanova's semi-home court advantage, with the Wildcats holding a 47% chance of winning. Sagarin's combined formula predicts a 4-point Villanova win, if the game is considered to be played on their home court, while the predictive formula alone calls for a 1-point Villanova win. Go Bruins!

 

<em>This is a FanPost and does not necessarily reflect the views of BruinsNation's (BN) editors. It does reflect the views of this particular fan though, which is as important as the views of BN's editors.</em>

X
Log In Sign Up

forgot?
Log In Sign Up

Forgot password?

We'll email you a reset link.

If you signed up using a 3rd party account like Facebook or Twitter, please login with it instead.

Forgot password?

Try another email?

Almost done,

By becoming a registered user, you are also agreeing to our Terms and confirming that you have read our Privacy Policy.

Join Bruins Nation

You must be a member of Bruins Nation to participate.

We have our own Community Guidelines at Bruins Nation. You should read them.

Join Bruins Nation

You must be a member of Bruins Nation to participate.

We have our own Community Guidelines at Bruins Nation. You should read them.

Spinner.vc97ec6e

Authenticating

Great!

Choose an available username to complete sign up.

In order to provide our users with a better overall experience, we ask for more information from Facebook when using it to login so that we can learn more about our audience and provide you with the best possible experience. We do not store specific user data and the sharing of it is not required to login with Facebook.

tracking_pixel_9347_tracker