Top Individual Performances (Week 3 MLAX)
Each week, we will look back at the games that were to see which players had the largest individual performances. I say largest because the contributions that we can measure (from play by play) tend to be things that are easy to count. This includes, goals, shots, assists, turnovers, penalties, etc. We can’t measure a defender who shuts down an opposing player so completely that he doesn’t even touch the ball. Still, it is interesting to be able to identify the players that really filled it up each weekend and give them a shout out here. | |
For a bit of background, in order to rank single game performances, we needed a way to condense box score stats to a single number for each player. In order to do this, we relied on our expected goal values methodology, which assigns a goal value to each type of play depending on how often it leads to a goal in the next 60 seconds. By adding up all the expected goals added for each player, we can get to that single number and these rankings. | |
We have also tagged each performance with the opponent’s ELO rating. The higher the number, the stronger the opponent. This should help to give some context for each performance. Did the player feast on the dregs of D1 or did they put up these numbers against a quality opponent? | |
Top Single Game Scores of the week |
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#1 – Ryan Tierney (Hofstra) – 9.28 goals added
Feb 27 vs Stony Brook Opp ELO: 1502
Ryan Tierney’s season is distinctly V-shaped so far. In game 1, he put up 11 points and 8.68 EGA against St. John’s. On Saturday, it was 12 points and 9.28 EGA against Hofstra. In between, it was a 2 point outing against Sacred Heart where he shot 1 of 13.
Sacred Heart does rate a bit better than the other 2 defenses (24th vs 31st and 38th), but not to the degree that you’d point that as the obvious reason for the V. As with so many things, the data can point out something, but not give a satisfactory answer as to why. Career Stat
Percentile
EGA/game
97th
Total EGA
99th
Offensive EGA
99th
Excess Goals
1st
eGoals/Shot
69th
Shooting Percentage
66th
Shot-on-Goal Rate
51st
Play Shares
99th
Goals
Assists
Shots (OG)
8
4
11 (9)
GBs
FO Won
FO Pct
1
0
N/A
Turnovers
Penalties
Caused TO
1
1
0
Career Stat
Percentile
EGA/game
97th
Total EGA
99th
Offensive EGA
99th
Excess Goals
1st
eGoals/Shot
69th
Shooting Percentage
66th
Shot-on-Goal Rate
51st
Play Shares
99th
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#2 – Chase Patterson (Hofstra) – 7.05 goals added
Feb 27 vs Stony Brook Opp ELO: 1502
Now, Tierney doesn’t get 12 points and Hofstra may not come away with the W without Patterson’s day at the faceoff X. The Pride enjoyed a +9 possession advantage in this game, primarily thanks to the 12 draw advantage he earned.
So far this season, Hofstra is +8.3 possessions per game and their highest FO win rate since at least 2015. And with the performance, Patterson has jumped to 19th among active FOGOs in the faceoff Elo ratings. Career Stat
Percentile
EGA/game
84th
Total EGA
54th
Faceoff EGA
72nd
Excess Goals
72nd
Shooting Percentage
31st
Faceoff Win%
93rd
Faceoff Elo
96th
Play Shares
82nd
Goals
Assists
Shots (OG)
0
1
1 (0)
GBs
FO Won
FO Pct
14
23
67%
Turnovers
Penalties
Caused TO
0
1
0
Career Stat
Percentile
EGA/game
84th
Total EGA
54th
Faceoff EGA
72nd
Excess Goals
72nd
Shooting Percentage
31st
Faceoff Win%
93rd
Faceoff Elo
96th
Play Shares
82nd
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#3 – Dylan Pallonetti (Stony Brook) – 6.74 goals added
Feb 27 vs Hofstra Opp ELO: 1537
The game that keeps on giving. Pallonetti rounds out the top-3 on this list who all happened to be from the same game. Frankly, it wasn’t a strong defensive showing. In some cases, you see certain games showing up a lot because the pace was crazy. But in this game, Stony Brook actually played at their slowest pace of the season and had the fewest offensive possessions of the season.
Regardless of why there were so many offensive fireworks, the point of this list is to highlight the players that set them off and once again Dylan Pallonetti finds himself in that group. He didn’t shoot as well as he did against Sacred Heart, but he made up the difference in this one with the 3 assists and 5 GBs. There are many ways to generate expected-goals after all. Career Stat
Percentile
EGA/game
99th
Total EGA
54th
Offensive EGA
68th
Excess Goals
37th
eGoals/Shot
65th
Shooting Percentage
76th
Shot-on-Goal Rate
58th
Play Shares
99th
Goals
Assists
Shots (OG)
5
3
14 (8)
GBs
FO Won
FO Pct
5
0
N/A
Turnovers
Penalties
Caused TO
0
0
0
Career Stat
Percentile
EGA/game
99th
Total EGA
54th
Offensive EGA
68th
Excess Goals
37th
eGoals/Shot
65th
Shooting Percentage
76th
Shot-on-Goal Rate
58th
Play Shares
99th
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#4 – Aidan Olmstead (Loyola) – 6.54 goals added
Feb 27 vs Utah Opp ELO: 1474
There was some thought out there that because Loyola was missing Bailey Savio in this one, Cole Brams and the Utes might end up with a possession advantage that could neutralize a bit of the gap between these teams. Well, that didn’t happen; Eric Pacheco and Chris Cottone combined to with 13 faceoffs and the Greyhounds actually ended up with a +6 possession advantage overall.
Olmstead led the offense in taking advantage of all those chances as they put up their best offense performance of the season (47.6% efficiency) against a Utes defense that rates in the top-15 on an opponent adjusted basis. For his part, Olmstead’s role in the Hounds’ offense has continued to swell. He picked up a lot of the slack when Chase Scanlan left for Syracuse, and the has continued through to this year. The difference for Olmstead (by extension) and Loyola is that he’s been more productive with his chances this year. Shooting percentage is up from 22.0% to 26.9%. Assists/gm is up from 1.83 to 2.33. His expected vs actual goals ratio, while still not great, is up as well. The only downside is that his individualized turnover rate is a bit up this year from .76 to .87. Olmstead may be the bellweather for the Loyola offense this year. So far, the signals are pointing in the right direction. Career Stat
Percentile
EGA/game
94th
Total EGA
95th
Offensive EGA
96th
Excess Goals
2nd
eGoals/Shot
49th
Shooting Percentage
51st
Shot-on-Goal Rate
37th
Play Shares
96th
Goals
Assists
Shots (OG)
5
3
11 (5)
GBs
FO Won
FO Pct
1
0
N/A
Turnovers
Penalties
Caused TO
2
0
0
Career Stat
Percentile
EGA/game
94th
Total EGA
95th
Offensive EGA
96th
Excess Goals
2nd
eGoals/Shot
49th
Shooting Percentage
51st
Shot-on-Goal Rate
37th
Play Shares
96th
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#5 – Joe Robertson (Duke) – 6.22 goals added
Feb 27 vs Air Force Opp ELO: 1531
Two things are true about Joe Robertson’s season. One, his role in the offense is diminished from what it was when he lasted suited up in 2019. And for obvious reasons. Two, his efficiency in his role has never been higher.
In 2019, his weighted play share was 9.9%; this year is it 6.4%. Again, not a huge surprise. But anything he may have lost in chances, he’s made up for in efficiency because he’s shooting at a higher percentage, converting more often than expected, dishing out more assists, turning the ball over less and generally playing better than he ever has. Can’t speak for Joe, but I’m guessing it’s a trade he’d make again. Career Stat
Percentile
EGA/game
94th
Total EGA
95th
Offensive EGA
97th
Excess Goals
24th
eGoals/Shot
83rd
Shooting Percentage
85th
Shot-on-Goal Rate
69th
Play Shares
89th
Goals
Assists
Shots (OG)
7
3
12 (11)
GBs
FO Won
FO Pct
1
0
N/A
Turnovers
Penalties
Caused TO
1
0
1
Career Stat
Percentile
EGA/game
94th
Total EGA
95th
Offensive EGA
97th
Excess Goals
24th
eGoals/Shot
83rd
Shooting Percentage
85th
Shot-on-Goal Rate
69th
Play Shares
89th
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#6 – Matt Campbell (Villanova) – 6.19 goals added
Feb 27 vs Marquette Opp ELO: 1487
There is probably no player who has stepped up more in the absence of Connor Kirst than Matt Campbell. And he’s managed the workload well. Through two games for Villanova, he’s shot 27.3%, compared to 21.1% last year.
And in games where he didn’t play against the buzzsaw that is Georgetown’s defense, the numbers are really impressive (cherry-picking? of course). The Hoyas did a good job limiting Campbell’s chances as he had just a 6.8% play share in that game. Against Marquette, that figure was 13.3%. Georgetown forced him into 3 turnovers and just 5 shots. Against Marquette, his stat line was clean and he was the primary option, taking 17 shots (most on the team). Career Stat
Percentile
EGA/game
94th
Total EGA
88th
Offensive EGA
91st
Excess Goals
5th
eGoals/Shot
52nd
Shooting Percentage
51st
Shot-on-Goal Rate
26th
Play Shares
97th
Goals
Assists
Shots (OG)
5
2
17 (9)
GBs
FO Won
FO Pct
1
0
N/A
Turnovers
Penalties
Caused TO
0
0
0
Career Stat
Percentile
EGA/game
94th
Total EGA
88th
Offensive EGA
91st
Excess Goals
5th
eGoals/Shot
52nd
Shooting Percentage
51st
Shot-on-Goal Rate
26th
Play Shares
97th
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#7 – Jonathan Dugenio (Rutgers) – 6.16 goals added
Feb 27 vs Ohio State Opp ELO: 1682
Kirst is the guy that gets the plaudits, but Dugenio could easily end up having a bigger impact for the Scarlet Knights this year. And here’s why; for the first time since 2017, they are out-possessing their opponents. Dugenio went toe-to-toe with Justin Inacio and played the Buckeyes FOGO to a literal draw (pun intended).
For the season, Rutgers has a +3 per-game possession advantage. Last year, it was -5. Using a league average efficiency, a 8-possession margin should net you between 2 and 3 goals per game. Possessions has been an issue for Rutgers for years, but now with Dugenio in the mix, that may no longer be the case. Career Stat
Percentile
EGA/game
99th
Total EGA
82nd
Faceoff EGA
87th
Excess Goals
85th
Shooting Percentage
86th
Faceoff Win%
83rd
Faceoff Elo
92nd
Play Shares
97th
Goals
Assists
Shots (OG)
2
0
3 (2)
GBs
FO Won
FO Pct
5
17
50%
Turnovers
Penalties
Caused TO
0
0
0
Career Stat
Percentile
EGA/game
99th
Total EGA
82nd
Faceoff EGA
87th
Excess Goals
85th
Shooting Percentage
86th
Faceoff Win%
83rd
Faceoff Elo
92nd
Play Shares
97th
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#8 – Brendan Nichtern (Army) – 6.09 goals added
Feb 27 vs Saint Joseph’s Opp ELO: 1545
Army’s offense has taken an interesting turn so far this year. For the first time since I’ve tracked this stat, their team assist rate is below 50%. Last year’s team assisted on 68.2% of their goals. This year, it’s just 45%.
That’s not necessarily a bad thing. Assisted goals require more things to go right than unassisted goals. I’ve long thought (although yet to prove) that offenses that are reliant on assists are going to have a harder time over the course of a season than offenses that can just go get goals by having a single player make it happen. One possibly related benefit for Army is that their team turnover rate is down from 36.6% last year to just 30.2% this year. It’s only 2 games, so we’ll see what happens with this, but it’s an interesting trend so far. Career Stat
Percentile
EGA/game
98th
Total EGA
95th
Offensive EGA
97th
Excess Goals
5th
eGoals/Shot
61st
Shooting Percentage
60th
Shot-on-Goal Rate
45th
Play Shares
99th
Goals
Assists
Shots (OG)
4
3
9 (6)
GBs
FO Won
FO Pct
2
0
N/A
Turnovers
Penalties
Caused TO
3
0
0
Career Stat
Percentile
EGA/game
98th
Total EGA
95th
Offensive EGA
97th
Excess Goals
5th
eGoals/Shot
61st
Shooting Percentage
60th
Shot-on-Goal Rate
45th
Play Shares
99th
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#9 – Mike Robinson (Delaware) – 5.99 goals added
Feb 23 vs Saint Joseph’s Opp ELO: 1557
This was a pretty wild performance. Robinson flirted with the all-time goals-in-a-game record for a while. In the end, it was merely an awesome game. The 75% shooting percentage was a career high. He generated more EGA in 12 shots than he did against NJIT the previous weekend with 17.
Of course, assisted goals, of which he had many, split EGA credit between the goal scorer and the assister, which is how Mike Robinson is only 9th on this list. Career Stat
Percentile
EGA/game
98th
Total EGA
76th
Offensive EGA
81st
Excess Goals
19th
eGoals/Shot
66th
Shooting Percentage
76th
Shot-on-Goal Rate
69th
Play Shares
98th
Goals
Assists
Shots (OG)
9
0
12 (10)
GBs
FO Won
FO Pct
3
0
N/A
Turnovers
Penalties
Caused TO
0
2
0
Career Stat
Percentile
EGA/game
98th
Total EGA
76th
Offensive EGA
81st
Excess Goals
19th
eGoals/Shot
66th
Shooting Percentage
76th
Shot-on-Goal Rate
69th
Play Shares
98th
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#10 – Mike Sisselberger (Lehigh) – 5.92 goals added
Feb 27 vs NJIT Opp ELO: 970
Wouldn’t you know it, Sisselberger won 10 more faceoffs than he lost and Lehigh had 10 more possessions than NJIT. Whatever you think of the faceoff, I still think the importance of possession margin is under-appreciated. Time-of-possession, in my mind, is an almost worthless stat. I does not matter how long you have the ball, it matters how many more times you have the ball than your opponent.
Lehigh has always been solid at the faceoff x, and while I don’t expect them to be +10 possessions every game, this was not an anomalous performance for the Hawks. Career Stat
Percentile
EGA/game
92nd
Total EGA
63rd
Faceoff EGA
75th
Excess Goals
46th
Shooting Percentage
41st
Faceoff Win%
91st
Faceoff Elo
94th
Play Shares
92nd
Goals
Assists
Shots (OG)
1
0
2 (1)
GBs
FO Won
FO Pct
10
16
72%
Turnovers
Penalties
Caused TO
0
0
0
Career Stat
Percentile
EGA/game
92nd
Total EGA
63rd
Faceoff EGA
75th
Excess Goals
46th
Shooting Percentage
41st
Faceoff Win%
91st
Faceoff Elo
94th
Play Shares
92nd
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March 2, 2021 @ 9:10 pm
Are you ever going to do mens D2?
March 3, 2021 @ 5:33 am
Unclear. I am definitely going to look into doing D2 and D3. The open question is whether the data is as available and reliable as it is for D1.