Offensively, the interesting point to note is that the simultaneously slowed down AND reduced their turnover rate. As we’ve documented before, slower pacing tends to increase turnover rates. But UMass reduced their turnover rate from 36% (not that bad) to just 6% (1 turnover in 18 possessions) in making their comeback. Before that point, their 1st shot came after an average of just 27 seconds; after, it was 39 seconds.
And their shooting percentage jumped from 24% to 44%, which suggests that in slowing down, they were able to find better looks against the LIU defense. And coming in, the LIU defense had better more effective on shorter possessions than on longer possessions, so it makes sense that the UMass strategic change might have yielded some benefits.
But OSU scored the last two goals, Hopkins turned their offense around, and with the PSU snoozer against Furman, it ended up being a 3-0 day for the league.
The Buckeyes averaged 47 seconds before taking their first shot in this game, which was, by far, their slowest pace of the season. But it worked out for them in the end as they grabbed a key win looking ahead toward Selection Sunday.
Consider this, the Mount’s first shot in building a 10-7 lead came after 32.5s. Over the final 11 minutes, it was 40s. Higher, sure, but it’s not like they were waiting until there were 5 seconds on the shot clock. What’s more, their shooting percentage and offensive efficiency were actually better after they supposedly went conservative.
No, what really stands out is how the JHU offense flipped a switch. Over the final 11 minutes plus overtime, the Jay’s improved their shooting percentage from 23% to 60%. Their SOG rate jumped from 58% to 90%. Their turnover rate fell from 56% to 15%.
The goalie turnover and subsequent goal was fluky, but the story of the Mount collapse is maybe one of tired legs. It’s almost certainly not one of an ultra-conservative strategic error. Give Hopkins credit for turning around an offensive performance that had been lackluster to that point.
To rank games, we will be using three values:
- Tension Game Score – a measure of how tight the game was. A back-and-forth match up where neither team is able to take control is going to have a lower tension score (i.e. smaller gap between the teams).
- Min. Win Probability – the lowest win probability value that our model ascribed to the eventual winner during the course of the game. A game where the winner had to make a huge comeback is probably going to have a very low win probability (think about the moment that it seemed least likely that they’d come back).
- Lax-ELO Transfer – a measure of how much an intridual game did to make us re-assess how good a certain team was. In a game where a favorite beats the underdog by as much as we thought they would, you don’t really update your priors about how good each team is. Those games would also transfer a relatively small number of Lax-ELO points.
We will present the metrics for each game, along with a star-based indicator to tell you how common or uncommon that outcome is. For example, a game in which the winning team had a win probability in the single digits is a rare occurence and would get 3 stars in that category.
We welcome your feedback, and we hope that this helps you to distill down the craziness of a full lacrosse slate into a more digestible list of highlights.
