Previewing the 2021-22 MIAA Season

All eight MIAA men's basketball teams have completed their non-conference schedules and will move to conference play beginning on January 5.

Coming into the season, Trine was the heavy favorite after returning nearly everyone from the team that went 17-1 (5-0 MIAA) and won the conference tournament in the COVID-shortened 2020-21 season. That expectation has perhaps shifted after a non-conference season that saw Trine lose four games (and record a few uncomfortable wins against inferior competition).

Calvin has seemingly supplanted the Thunder as the league favorite as league play begins on Wednesday. Here's a look at where each team stands in the efficiency ratings I run for all of Division III men's basketball:

 

D3 Rank	Team	AdjEM	AdjO	AdjD
25	Calvin	22.4	113.8	91.5
72	Albion	13.8	104.7	90.9
70	Trine	14.0	101.4	87.5
77	Hope	12.6	108.5	95.9
167	Olivet	3.0	96.8	93.8
258	Kalamazoo	-3.7	101.3	105.0
267	Adrian	-4.5	97.2	101.7
297	Alma	-8.1	96.6	104.6

A quick explainer for those who haven't followed my ratings: Adjusted efficiency margin is the difference between a team's adjusted offensive and defensive efficiency ratings (the 'adjusted' means the calculations are iterated to account for quality of opponent). This is the number of points they'd be expected to outscore an average D3 team in 100 possessions. The offensive rating (AdjO) is how many points they'd be expected to score (per 100 possessions) and the defensive rating (AdjD) is how many points they'd be expected to give up (per 100 possessions).

So based on results* to date, we have something like four tiers of teams forming in the MIAA. Calvin is alone in tier 1 as a (fringe) Top-25 caliber team, Albion, Trine, and Hope are bunched up very tightly in tier 2, Olivet is in their own tier a bit above D3 average, and Kalamazoo, Adrian, and Alma are in the fourth tier a bit below average.

*I only calculate data from D3 vs. D3 games so nothing against D1, D1, or NAIA is included here.

The cool thing about these efficiency ratings is that we can use them to predict scores for individual matchups. If we add in a few points for home court advantage, we can predict the outcome of any game. So, for example, my computer favors Calvin over Albion in Wednesday's conference opener 74-72 and gives Calvin a 59% chance of winning the game. If I run these percentages for all 56 conference games, I get the following expected standings:

 

Team	Expected Wins	Expected Losses	Expected PF	Expected PA
Calvin	11.8	2.2	1148	957
Albion	9.5	4.5	1061	969
Trine	9.5	4.5	1000	910
Hope	9.1	4.9	1078	1001
Olivet	6.0	8.0	1060	1098
Kalamazoo	4.0	10.0	967	1076
Adrian	3.4	10.6	1065	1203
Alma	2.7	11.3	960	1124

Now, obviously, the standings won't end up exactly like this. Someone (Olivet) is going to win some games they're not supposed to win and we'll likely end up with more bunching towards the middle as teams get familiar with each other. But that's the way the computer sees the league shaping up before we play the games.

For Calvin in particular, they're going to have to make hay the first time through the league schedule as they have Hope, Trine, and Olivet at home and only Albion on the road of the other MIAA teams with a positive adjusted efficiency margin. They'll really need to be 5-2 (or preferably better) in those first seven games to feel really good about their chances to win the regular season conference title.

Looking ahead to the NCAA Tournament, the MIAA is most probably a one-bid league. It's not likely that the tournament runner up will have the resume required to receive an at-large bid. Most of the league is exiting non-conference play with a strong strength of schedule, but the league's overall non-conference record means those SOS numbers are going to decrease as we go.

I see Calvin, Hope, Albion, and Trine all staying a fair bit above the .500 strength-of-schedule Mendoza line that is the de facto requirement for NCAA Tournament selection, but not so far above that they'll be a strong contender unless they're also touting winning percentages in the .850 range (or so). That's... probably not going to happen. The best case for an at-large berth from the MIAA is for one of these teams to go 12-2 or 13-1 in conference play and then lose in the conference tournament final, and even that might not be enough to escape the bubble.

Turning our attention to opening night on Wednesday, it looks like we have a great slate of competitive games. Here's what my score predictor expects:

Away		Home		Home %	Away%	Pace	Line	Total
Olivet	86	Adrian	83	40%	60%	89	+3.0	169.5
Trine	66	Hope	68	60%	40%	69	-3.0	134.0
Kalamazoo	72	Alma	73	53%	47%	69	-1.0	145.0
Calvin	74	Albion	72	41%	59%	72	+2.5	146.0

All four games are 60%-40% or tighter and none of the expected score differentials are wider than one possession. The winners -- particularly of Trine-Hope and Calvin-Albion will have a leg up in the conference race. It should be a good night of basketball. 

Definitive Proof That The NCAA's "New" SOS Calculation Method Is Wrong

We had a revelation this week courtesy of Dave McHugh of D3hoops.com (and Hoopsville). The NCAA Championships Committee has changed the way they're computing the strength of schedule component that is a part of the primary selection criteria for the NCAA Tournament.

Strength of schedule (SOS) is made up of two components: opponents' winning percentage (OWP) and opponents' opponents' winning percentage (OOWP). They're calculated in a similar manner, but the concept of OWP is easier to grasp and "see" so I'll focus on that one component for the purpose of this blog post (but you can apply the same principles to OOWP).

The NCAA realizes that home games and road games differ in difficulty, so they've decided to add in a multiplier (to both OWP and OOWP) of 0.75 for home games, 1.25 for road games, and 1.00 for neutral site games. This isn't a change from last year.

Anyway, the old way to compute OWP and OOWP (I'll also call it the correct way) was computed using the average of the opponents' W/L percentage. For example, if a team played two teams with records of:

at home vs. 3-1 (.750)
on the road vs. 4-2 (.667)

Their OWP was calculated to be [ (.750 x .75) + (.667 x 1.25) ] / 2 = .698.

The new way (I'll also call it the incorrect way) is computed using the sum of the opponents' wins and losses to come up with an overall percentage. So for the same two teams above it would be [(3 x .75) + (4 x 1.25)] / [(4 x .75) + (6 x 1.25)] = .690.

This small example is only to show that there is a difference between the two calculation methods. It's only a two-game portion of a schedule, and a .008 difference isn't huge, but let's consider another example.

Opponent	W	L	PCT	Location	MULT	Old OWP	New W	New L	New OWP
Team A	19	1	0.950	Away	1.25	1.1875	23.8	1.3	-
Team B	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team C	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team D	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team E	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team F	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team G	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team H	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team I	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team J	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team K	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team L	2	10	0.167	Away	1.25	0.208	2.5	12.5	-
TOTAL						0.429	86.3	73.8	0.539

So it's a road game against a really good 19-1 team, a bunch of home games against 8-8 teams, and a road game against a bad 2-10 team. The old method gives an OWP of .429 and the new method gives an OWP of .539. Which is right? It's hard to say, but all those home games against .500 teams makes it seem like it should be a below .500 OWP. I like the first method. But that's really not my point. The real point I'm going to make comes when we flip all those home games to road games.

Opponent	W	L	PCT	Location	MULT	Old OWP	New W	New L	New OWP
Team A	19	1	0.950	Away	1.25	1.188	23.8	1.3	-
Team B	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team C	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team D	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team E	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team F	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team G	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team H	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team I	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team J	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team K	8	8	0.500	Away	1.25	0.625	10.0	10.0	-
Team L	2	10	0.167	Away	1.25	0.208	2.5	12.5	-
TOTAL						0.637	126.3	113.8	0.526

It's an all-road schedule against nine teams that are at least .500 and one team below .500. The old method says this is a decently tough schedule and gives it a .637 OWP. The new method says it's only a decently tough schedule and gives it a .526 OWP.

BUT WAIT! The new method gives a lower OWP value for the all-road schedule (.526) than it did when eight of the games were at home (.539)!

We we simply sum the games up like this (instead of taking the average of the percentage), we're not giving each game (opponent) equal weight in our OWP calculation. This is especially true in conjunction with the home/away multiplier (sometimes known as HAM). In the "new" method, the HAM doesn't make it so road games are "tougher" than home games, it just makes it so road games weigh more heavily than home games. This is probably the opposite of what should be true.

Want even more fun? Let's make all those games in the above example home games.

Opponent	W	L	PCT	Location	MULT	Old OWP	New W	New L	New OWP
Team A	19	1	0.950	Home	0.75	0.7125	14.3	0.8	-
Team B	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team C	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team D	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team E	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team F	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team G	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team H	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team I	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team J	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team K	8	8	0.500	Home	0.75	0.375	6.0	6.0	-
Team L	2	10	0.167	Home	0.75	0.125	1.5	7.5	-
TOTAL						0.382	75.8	68.3	0.526

Yeah, so the "new" method gives an all-home schedule the exact same OWP as when they were all road games. That's because the HAM is only scaling the number of games in the new method, it's not actually changing the perceived difficulty of the game (the percentage).

This isn't just my opinion, this isn't just a different way to do things, this is simply wrong.

Who’s Shooting The Ball, And From Where?

I had a bit of free time this offseason, so I spent a portion of it combing through box score play-by-play data (I can tell you’re interested already) to see if I could compartmentalize shot selection. The traditional box score has “buckets” for three pointers and total field goals, so it’s easy to separate out into two-point shots and three-point shots, but I wanted to try to unearth another bucket or two, because an 18 foot two-point jump shot is different than a layup in terms of makeability (spell check is telling me this isn’t a word, but I just typed it, and you understood it, so it must be).

I noticed in Calvin’s play-by-play data that there are four types of shot labels: three-pointer, jumper, layup, and dunk. So, I thought the easiest thing to do was to load each game’s play-by-play section into Excel and have it count made and missed “jumpers” for each player for me. This effectively separated mid-range shots from close to the basket attempts and, along with the readily available three-point numbers, gave me three total shot-type buckets to work with. I would have liked to separate out jump shot range further – perhaps something as simple as in the paint and out of the paint – but the data only labeled as shot as in the paint ([PNT]) if it was a made basket, so that idea didn’t work.

Of course, the data is not perfect (technically I’m supposed to write the data are not perfect, but I find that to result in an unnecessarily snooty tone) because it’s entered manually by humans at the scorer’s table who are trying to watch and do about seven hundred things at once, so some of the labels aren’t perfect. For example:

00:04 63-64 V 1 GOOD! LAYUP by Ryan Krombeen [PNT]

Sorry to dig up the bad memory (sad face), but it served a purpose.

I’m not sure I would have necessarily called that a layup myself, but that’s how it was recorded, and we’ll live with it. It’s not really the type of mid-range jumper I was looking to separate out either, so it’s probably not a big issue. I can imagine that a type of observational bias may exists here whereby made shots of this type are more likely to be dubbed “layups” and missed shots are more likely to be “jumpers” (because a made shot looks easier). That’s not a knock against the folks that enter the data, these plays have to be made with real-time split second decisions, but it should be noted that the play-by-play data isn’t gospel. (One often sees this phrase with gospel meaning something like “divine truth”, as I just used it, but the word literally means something like “good news”, so these statements never really make any sense.)

Anyway, I should probably present some numbers before this post reaches the 500 word mark and you, the reader, go all close window, delete bookmark on me. Here are the combined team numbers for the 2012 season:

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2012 First Half/Second Half: Brian Haverdink

Previously: Tyler Dykstra

When I first decided to check out first half/second half splits for certain players Haverdink didn’t come to mind. My initial focus was going to be on a few of the less experienced guys. So I fired off the first post on Tyler Dykstra* and was going to follow up with Jordan Mast, but one fact kept gnawing at me while looking through Mast’s numbers: despite better offensive numbers during the second half of the year, he saw a playing time reduction.

*The author is keenly aware that it has been well over two months since that post. Don’t judge him.

This fact didn’t make sense for two reasons. (1) As I stated above (and will show in a future post), Jordan’s offensive numbers took a big jump in the second half of the year, and (2) Mitch Vallie and Mickey DeVries each missed the entire second half of the year and David Rietema missed a few games as well. These three players don’t fit the exact Jordan Mast archetype, but you’d think that there would have been some position shifting involved that would force Mast (who even started four games) into more playing time. But that didn’t happen; Mast’s per-game average fell from 18 minutes to 14.8 minutes between the first 13 games and the second 13 games. So, yeah. I didn’t get it right away.

I didn’t really have a feel for this at the time, but Brian Haverdink’s minutes soared in the second half of the year. I mean, I knew he was re-inserted into the starting lineup just a few games into conference play, but I don’t know that it really struck me that he was getting so many more minutes than he received during most of the non-conference season. But he certainly was.

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2012 First Half/Second Half: Tyler Dykstra

I usually live in the land of cumulative season statistics (I’m a sucker for large sample sizes), but it struck me today that it would be interesting to go through the Individual Game-by-Game Box Scores on the Calvin Sports page to try to get a glimpse at how some of the players progressed throughout the season. You could pretty much pick any range of games to do this, but I decided to stick with a simple 50-50 first half/second half approach (because small sample sizes tend to frighten me). This is easy enough considering Calvin played an even number of games (26).

I picked Tyler Dykstra to profile first, but I’ll admit to not choosing completely randomly (though his name immediately came to my mind). A quick glance at his numbers confirmed my thought: although he was never a big scorer, he was a bigger part of the offense as the year went on. He’s an ideal candidate to track development though. He’s young, he played in nearly every game (25 of the 26), and his length and athleticism leads to lots of potential.

32 - Dykstra	G	Min	MPG	Pts	PPG
1st Half	12	108	9.0	6	0.5
2nd Half	13	204	15.7	63	4.8
Total	26	312	12.0	69	2.8

As I said, he never became a huge part of the offense, but I was floored to realize that he only accumulated six points through the team’s first 13 games (he only played in 12 after not appearing in the opener at Anderson). But this story isn’t about his early season struggles; it’s about his strong finish to the season. Here are his shooting numbers for the year:

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Stats-and-More Versus Olivet

Win Expectancy

Calvin’s win expectancy jumped from 50% (where all games start) to 71% just ten seconds into the game as Calvin took a 2-0 lead on a Tom Snikkers field goal. It wouldn’t dip below 70% for the rest of the game.

This was a textbook wire-to-wire beat down, with the win effectively secured with five minutes still to play in the first half. There was really no point in this game in which Olivet made an actual run at competitiveness. Their best stretch of multi-possession sustained play might have been keeping the Calvin lead right around seven from the 13:09 mark to the 9:44 mark in the first half.

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Stats-and-More Versus Alma

Win Probability

Alma took a 9-3 lead about four minutes into the game, but it was a steady climb from there on to the win. Despite the student webcast announcers calling the game a ‘nail biter’, nothing really exciting happened in the final twelve or thirteen minutes.

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Stats-and-More versus Kalamazoo

Win Probability

I’ve highlighted a few of the ‘bigger’ plays of the game. For Kalamazoo it was Adam Peter’s two point shot in the first half that gave the Hornets a six point lead – it would be their biggest lead of the evening – the win probably graph gave Calvin only a 30% chance of coming back to win at this point.

Calvin’s top play of the night was a three point play by Tom Snikkers just one minute into the second half. David Rietema came up with a steal and found Snikkers streaking toward the hoop. Snikkers received the pass, drew contact, and laid the ball up and in. He hit the free throw to give the Knights a three point lead – a lead they would not relinquish. This play netted Calvin 30% in the win probability column, from about 50% up to about 80%.

The final play I called out was a jump shot by Bryan Powell that gave the Knights a nine point lead with under ten minutes to play. The result of the play added about 14% to Calvin’s win probability total, bumping their percentage up to 96%.

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Stats-and-More Versus Adrian

Win Probability

Adrian jumped out to a 3-0 lead, and it was Calvin playing catch up pretty much the rest of the way. Despite taking a brief one-point lead, and cutting the Adrian margin to one possession on a couple of occasions, they were never able to get their win probability above 45%.

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According to Massey Ratings, Calvin Underperformed Early This Season, Have Overperformed Recently

I won't pretend to know all of the intricacies of Kenneth Massey's "Massey Ratings" (one of the computer rankings used in college football's BCS rankings), but I do know most the basics. For each game (college basketball, in our case), the score, venue, and date are used as inputs into the computer system. The computer then does a massive 'best fit' calculation of sorts for every game played in the college basketball universe, and comes up with a rating for each team.

For a real explanation, visit Massey's FAQ and Theory pages.

One of the more interesting aspects of his ratings website is perhaps the predictions that his ratings allow. For instance, we see that Calvin is projected to have only a 16% chance of beating Whitworth tomorrow, and that the 'most likely' score is 78-68 in favor of the Pirates. That's cool and all, but what I'm going to look at are his retro-active predictions for the Knights.

As the season goes on and more data points are added to the system, the ratings for each team are adjusted, and so are the predictions for the games that have already happened. For instance, just before Calvin and Finlandia met on the eve of Thanksgiving, Massey said that Calvin was expected to win by 25 points. Now, a month later, the prediction page says that Calvin should win by 14.

I went back through the nine games that the Knights have played to see how the actual score lines up with the current "retro" prediction:

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At Least The Olivet Comets Have One Good Player

The popular opinion was that the Olivet Comets were going to struggle this year. They were losing possibly the most dominant player the MIAA had seen in a decade, Michael McClary, along with solid contributors Nathan Jennings and Joe Post (and they had already lost Andre Evans in the middle of the season). To me, it seemed like they had little hope of finishing above seventh place in the league this year.

But, as we all know, things don’t always pan out the way we think they will.

But, in this case, it looks like it did. It’s exactly as we thought.

The mass departure of the above players left the Comets with a severe lack of talent. Only Junior Alvino Ashley (a transfer from Lansing Community College) has stepped up to play like a bona fide starter. Senior Jaren Edsall is playing well enough to contribute as a starter, but he’s being counted on to be a major factor (and he’s just not that).

Here’s a look at the Olivet Comets (as described by Player Efficiency Rating).

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Stats-and-More Versus Trinity

Win Probability Graph

This game was never close to ever being in doubt. Calvin's win probability hit 98% one minute into the second half, and hit 100% about two minutes after that. Even while Calvin's 30 point lead was in the process of being trimmed all the way down to 17, the number never wavered below 100%. Obviously 100% is never 100% unless the clock says zero-zero, but it in this case 100% means the team that's ahead will lose the game in less than 1 time in 10,000 identical situations (or, that's what it would say if our data set had an infinite number of games).

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Stats-and-More after the MIAA-CCIW Classic

Win Probability Graphs

Wins look like smiles.

And losses are frowns. Actually, this one looks like falling off a cliff, which is what it felt like at the time.

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Calvin, the MIAA, and Player Efficiency Ratings

I’m a sucker for one-number metrics. In basketball, I love to judge teams by RPI; in baseball I love to judge players by WAR. Fans often have reservations when it comes to describing the quality of play of an entire team or individual by a single number (surely you can’t measure and quantify every single contribution or intangible!), but, at the same time, we’re obsessed with (somewhat) ordered lists and rankings.

Top 25 polls, All-Americans, players of the week, regional rankings, MVP selections, all-tournament teams, and all-conference teams are part of our “normal” routines. Each of these require us to either measure and quantify (or simply ignore the “unquantifiable”). In selecting or debating these types of awards and rankings, we are forced to determine what, exactly, is important to us.

Single-number ranking systems don’t (or shouldn’t) claim to be perfect. Just like your favorite top-25 ranking will always have flaws, an RPI ranking system will have flaws. But unlike your favorite top-25 (or all-conference team or whatever), an RPI system (or player efficiency ratings, or whatever) will always apply the criteria evenly across the board. Again, not perfect, but consistent (that’s always the key!).

Anyhoo, going back to my love of WAR (wins above replacement) in baseball, I began to play around with similar numbers for basketball. I found John Hollinger’s NBA player efficiency rating (PER) a few years ago, and I’ve been toying with it to adapt it to the MIAA since. PER is a convoluted formula (so I won’t try to explain it here), but I think it does a pretty good job of organizing players in an expected way. For example, in each of the last two MIAA seasons, PER “correctly predicted” 10 of the 12 all-conference players (and it also did well in predicting first/second team).

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Stats-and-More After The GRSHOF Classic

I didn’t actually see this weekend’s games, so I won’t comment too much on them, but we can look at where this team currently is statistically and go from there.

Season Stats

Player	%MIN	%Shots	eFG%	PPWS	FTr	ORb%	Rb%	Ar	TOr	Blk%	Stl%	OEff	DEff
Snikkers	0.64	0.35	0.375	0.88	0.36	0.05	0.10	0.15	0.14	0.02	0.03	-	-
Kruis	0.62	0.15	0.622	1.44	0.81	0.11	0.17	0.06	0.15	0.07	0.01	-	-
Haverdink	0.60	0.15	0.329	0.73	0.11	0.06	0.05	0.09	0.10	0.02	0.01	-	-
Vallie	0.52	0.15	0.565	1.20	0.39	0.06	0.10	0.09	0.15	0.02	0.01	-	-
Rietema	0.50	0.12	0.479	1.08	0.50	0.01	0.05	0.45	0.17	0.00	0.02	-	-
DeVries	0.47	0.21	0.500	1.03	0.51	0.11	0.12	0.09	0.07	0.02	0.02	-	-
Powell	0.45	0.28	0.410	0.86	0.14	0.03	0.08	0.32	0.17	0.00	0.02	-	-
DeBoer	0.45	0.30	0.566	1.15	0.08	0.07	0.10	0.19	0.07	0.00	0.01	-	-
Mast	0.37	0.13	0.395	0.85	0.11	0.07	0.10	0.09	0.11	0.05	0.02	-	-
Dykstra	0.14	0.15	0.125	0.25	0.00	0.06	0.06	0.05	0.15	0.09	0.00	-	-
DeYoung	0.11	0.07	0.667	1.33	0.00	0.15	0.14	0.13	0.18	0.00	0.02	-	-
Nadeau	0.06	0.00	0.000	0.00	0.00	0.00	0.04	0.21	0.00	0.00	0.00	-	-
Van Eck	0.04	0.12	0.000	0.34	1.00	0.00	0.10	0.14	0.00	0.00	0.00	-	-
Henry	0.02	0.51	0.000	0.00	0.00	0.21	0.21	0.00	0.00	0.00	0.00	-	-
Calvin	1.00	1.00	0.450	1.00	0.32	0.38	0.56	0.63	0.21	0.12	0.09	98.5	102.7
Opponents	1.00	1.00	0.513	1.08	0.30	0.26	0.44	0.53	0.18	0.04	0.11	102.7	98.5

Tom Snikkers is only shooting .341 on the season (down from .472 last year) – his effective field goal percentage is .375 compared to .498 a season ago – but contrary to what I had speculated a few days ago, his three point shooting isn’t the biggest problem at this point. No, Tom shouldn’t be tossing up three point shots early in the shot clock, but as of right now he’s ‘effectively’ shooting .409 on three point shots, and only .364 on two point shots. .409 isn’t good on three pointers, but it’s not the sole reason that Tommy is struggling this season. Defenses know that Snikkers has the potential to be Calvin’s most dynamic scorer on any night – and they’re keying in on him – but Tom isn’t adjusting his style of play. We’re still too often seeing the tunnel vision to the hoop when we could be seeing a Jeremy Veenstra like assist rate. Making it a bigger point to find his teammates, especially early in games, may help open up the defenses later on.

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Stats-And-More Versus Finlandia

Win Probability Graph

The good news is that, for the most part, they closed out both halves pretty well. The bad news is that they were a little bit slow out of the gate in the first ten minutes (or so) of both halves.

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Stats-And-More Versus Willamette

Win Probability Graph

Calvin spent most of this game building up a lead, but it's easy to see Willamette's three quick runs in the second half that made the game a lot more interesting than it should have been. First, there was the 11-2 Bearcat run that turned a seven point Calvin halftime lead into a two point deficit to being the second half. Then, with about eight minutes to play, Willamette found an 8-0 run to even up the score. And, finally, they went off on a 10-2 run to take a one point lead with just over two minutes to play in the game. Those three sequences were the difference between the close win we saw, and the blowout we wanted.

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Stats-And-More Versus Grace Bible

Win Probability Graph
This one is a lot more fun to look at than the one from the Anderson game (click image for larger version.).

Lots of ups and downs and back and forths. The lowest point of the game was right near the end of the first overtime when Calvin was down by five with under a minute to play (look just to the left of the 45 minute mark on the graph). At that point, their chances of winning was one-half of one percent. Snikkers' first three pointer brought that up to 11%, but then that dropped to 3% after Krombeen hit one of the free throws. Tommy's second three pointer that tied the game, then, brought them all the way back to 50%. That one shot was worth 47 points of 'win probability added', which is pretty much a full team win in itself (since each team begins the game with a 50-50 chance, one win equals 50 points of win probability added, and a loss would equal -50 points of WPA).

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Stats-and-More versus Anderson

This isn’t going to be a fun game to look at, but we'll press on anyway. We’ll start with a new one. Here’s a look at Calvin’s (approximate) win expectancy graph* for this game.

That looks about as bad as it felt. The chart gave Calvin a 0% chance of winning after Brock Morrison hit a three pointer to give Anderson a 30-16 lead after just about 11 minutes of game time. Of course, their real chances weren't really zero, but close to it. They made a little run early in the second half to get their chances up to 10%, but that's as close as they got in the final 30 minutes.

*Data comes from Brian Burke of AdvancedNFLstats.com.

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Stats-and-More Versus Ferris State

Game Stats
If you want/need a refresher on some of the stats that I like to use, see these four posts to educate yourself.

Player	%Min	%Shots	eFG%	PPWS	FTr	Ar	TO%	Rb%	eff
Kruis	0.70	0.12	0.600	1.45	0.80	0.18	0.17	0.12	-
DeBoer	0.58	0.30	0.300	0.57	0.10	0.31	0.12	0.12	-
Vallie	0.53	0.10	0.667	1.63	1.33	0.00	0.00	0.13	-
Snikkers	0.53	0.43	0.500	1.19	0.62	0.11	0.10	0.11	-
Mast	0.53	0.07	0.500	0.77	2.00	0.07	0.17	0.05	-
Haverdink	0.50	0.17	0.800	1.46	0.20	0.09	0.13	0.03	-
Rietema	0.48	0.07	0.500	1.00	0.00	0.08	0.50	0.06	-
Powell	0.48	0.36	0.500	1.10	0.20	0.48	0.06	0.06	-
DeVries	0.43	0.16	0.500	1.21	0.50	0.10	0.00	0.13	-
Dykstra	0.13	0.55	0.500	1.00	0.00	0.00	0.00	0.11	-
DeYoung	0.13	0.00	0.000	0.00	0.00	0.29	0.50	0.11	-
Calvin	1.00	1.00	0.509	1.12	0.45	0.61	0.18	0.50	108.9
Opponent	1.00	1.00	0.458	0.99	0.49	0.13	0.18	0.50	99.2

A few things that jumped out at me after looking at the numbers:

• It really seemed as though Bryan Powell reigned in his shot attempts, but he still ended up firing up 36% of the teams shots while he was on the floor (his average for last year was 25%). The big difference was that he was efficient with his shots in this game and, although he was 0-3 on three pointers, none of his attempts were “bad shots”.
• Calvin struggled with turnovers on offense last year (they turned it over on 21% of their possessions last year), but against Ferris State, their turnover rate was just 18% (league average is 20%). Maybe that isn’t a huge difference in a single game, but taking care of the ball will be big for Calvin this year.
• The Knights paired a high free throw rate (.45) with a pretty good 77% free throw percentage. Calvin doesn’t have the reliable three point shooters that they’ve had in the past, so they’ll need to use their length and athleticism to get to the basket and get to the line (and knock the free ones down down) to find extra points.
• David Rietema didn’t necessarily impress me in the starting lineup. He’s not a scorer so his contribution will be in taking care of the ball and distributing it to his teammates. Three turnovers isn’t a terrible number, but he’s going to need to learn that he can’t dribble around/through everyone at this level.

Rotations
This one is new for me, but I think it’s pretty cool. Here’s a look at all of the player combinations that Calvin had on the floor at any point, and how they performed (sorted by minutes).

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