VBelo Scouting Report: January 4, 2023
They're back!
Today is the day! After 242 days of waiting, D-I Men’s Volleyball is back in action. With almost poetic timing (or cramming it in at the last minute), the pre-season AVCA coaches’ poll came out yesterday. To no one’s surprise, Hawai’i topped the charts with 21 of the 22 first place votes.1 There are some great teams on that list, but at first glance it appears that name recognition helps a lot to get votes. In particular, the ConfCarolinas didn’t receive any votes, which is ridiculous in light of how well North Greenville performed at the end of last season.2 I’ve said it before, but VBelo doesn’t care about reputation. It cares about weighting results appropriately.
In addition to the first VBelo projection of 2023, today’s newsletter will include the VBelo preseason rankings and a helpful interactive schedule for the 2023 season. Hopefully, this is just the beginning of some fun interactives brought to you by VBelo. But let’s take a look at the top 15 teams in VBelo to start the season.
Preseason Ranking
In the days leading up to the season, each conference profile showed the conference rankings. But now, let’s look at the top 15 teams in VBelo. I am going to use the same disclaimer that I used in all of the conference profiles.3
Disclaimer
VBelo only knows the information that is fed into it. Since there have been zero matches in 2023 so far, it doesn’t have a complete picture of teams yet. The best it can do is see where a team finished last season and adjust for players lost in the offseason. The model will learn more about these teams the same way we all will: seeing who wins matches. While these preseasons rankings don’t foretell where the teams will end the season (it can’t predict the future), it shows us where VBelo thinks they are starting from relative to the rest of the nation.
The conference profiles have already talked about most of these teams (so feel free to check those out.) It probably can’t be said enough, but Hawai’i is in a great position for this season. In fact, they are currently the favorite for every match this season even though they are facing the majority of the top 10 teams.
When I look at the top 5 VBelo teams, I think the coaches’ poll is overvaluing Long Beach and undervaluing Penn State. Don’t get me wrong, they are both great teams. But…I think Penn State has a lot to prove this season. VBelo can’t measure chips on the shoulder, but if it could Penn State would be off the charts.4
The VBelo top 15 also features two conferences that the coaches seem to have left off their ballots. Daemen from the new NEC comes in at a very respectable #12. From the Conference Carolinas, North Greenville is at #8 and Mount Olive rounds out the list at #15. I will admit, this might be a slight overestimation of these teams, but not having these conferences in the coaches’ poll at all is a shame. I firmly believe that at least one of them should legitimately be in the top 15.
Season Schedule
From what I can tell, there are 793 regular season matches scheduled for 2023.5 This is a 9.5% increase from last year! There was no easy way to look up matches by date range or team, so I decided to make one. Thanks to the powerful data visualization tools over at Tableau, I was able to use the data I already had to create an interactive schedule.
Right now, it shows every match in the 2023 season by date. If a match is played on a neutral court, the location is shown. Also, there is a column to let you know if the match is a conference matchup. The exciting part, is that all of the match projections are shown as well. Just as a warning, these are the projections if those two teams played right now. As matches are played, these projections will adjust and the results will be shown in the schedule.
If you have any ideas for making this schedule more useful, please let me know.
Match Projections
Non-Conference
Lincoln Memorial (48%) at #5 Pepperdine (52%)
Can LMU travel to Malibu and pull off the upset? VBelo likes Pepperdine but only slightly. LMU has won four straight IVA tournaments, but Pepperdine is coming into the season with a top 5 ranking. This is a fantastic match to open the season with two great programs.
Tweet of the Day
UCLA received 1 first place vote.
The team they beat, Princeton, is ranked 15th. Looking at the retention metric for both teams, they are losing about the same amount of experience.
If these numbers look different from the conference profile VBelo ratings, then you are very observant. In updating the code for 2023 matches today, I found a slight issue that effected the ending VBelo rating. I checked and it was only an issue of scale and does not affect the rankings.
There are no plans to add this into the model. That would be insane.
There are a handful of matches that have some discrepancies between the two teams schedules. Also, some teams don’t have all of their matches posted yet. All this to say, this number could and probably will change slightly.
Will know your choice of a K value when you update to the second generation of the teams' elo ratings. Every week, maybe, or will it be daily? Looks like the mean score is 1560ish and you use 400 in your probability calculation. The combination of 400 and such a tight range of the initial values result in win probabilities which hover closer to 50%, thereby reducing accuracy early in the season. ie. the logistic model is dampened too much to represent the variability in D1 & D2 Men's Volleyball outcomes, (Maybe OK for NFL football given its parity, but probably not in this landscape?) If keeping the 400, then I would suggest Morehouse closer to 1000 and Hawaii at about 2100 because I am nearly certain Hawaii would win closer to 499 out of 500 contests against Morehouse than the 490 out of 500 it currently forecasts. Either that, or maybe reduce the 400 by 25% in the p calculation. Over time, elo will certainly find its way. My concern might be your K will either be too low so that it will take too long to find its way, or be too high, thereby getting the distribution "better quicker" at the price of creating a recency bias later in the season because single game adjustments will be too large because of it. Even if you choose the K which is "juuuuuust right," as Goldilocks might put it, your model might not even be as precise as you'd want come time for conference play. Just some observations from a guy who does a modified elo to predict all the games, too. I wish I found your schedule before last weekend!
One other observation is that I would weigh conference pre-season polls by its coaches no less than any metric. My experience is the "wisdom of the expert crowd" outperforms a single metric more often than not, even the cool one you create to measure the movement of individual talent. Merging both equally probably gets to a version of the truth even closer to what it really is, thus guaranteeing the model will approach its equilibrium much sooner. Very cool site.
This is so great, TJ! Thanks so much for all of this! The composite schedule is such a nice thing to have on tap. Thanks again!