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Quantifying Impact: Looking at Missouri’s past to (potentially) see its future

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Using player and lineup data fleshes out the role each Tiger played last season, with Javon Pickett and Torrence Watson serving as a prime case study.

NCAA Basketball: Missouri at Florida Kim Klement-USA TODAY Sports

Over the next couple of weeks, a trio of freshmen will unpack their belongings in Columbia, and Missouri’s offseason program will ramp up.

The next five months are a time when fever dreams emerge during summer’s doldrums, and those grand visions make a certain degree of sense. Roster churn and a quartet of coaching changes have jumbled the SEC’s hierarchy. For Missouri, which returns seven players, those fluctuations create room to maneuver and ascend.

Along the way, MU coach Cuonzo Martin might convene a press availability, a gathering where we seize on scraps of information and extrapolate their meaning.

Forecasting the future, however, requires an understanding of the conditions as they exist right now. Fortunately, there’s ample data — KenPom, HoopLens and Synergy — to help quantify and plot a starting point. What good is projecting a rotation if no one knows what a player brings to the table?

With Kobe Brown’s commitment two weeks ago, MU likely locked its roster into place, and with it, the ability to do a rough calculation of what seven returners can provide. Using Synergy Sports’ data, we know that core’s offensive efficiency — 0.888 points per possession — would likely land in the middle of the pack in the SEC. Unsurprisingly, it also grades out as one the conference’s best defensively, allowing a paltry 0.787 PPP. Climbing the standings likely hinges on player development and key additions like Dru Smith propelling the offense forward.

Earlier this spring, we did quick reviews of how the season went for each member of the roster, but we can be far more granular.

Why go through the process of calculating a player’s on-court impact? Above all, it starts any discussion with an objective set of facts. Those values can give us an understanding of why certain lineups thrived and others struggled. Finally, we can try to asses how one player’s skill set impacts their teammates.

A simple method makes it possible: compare Mizzou’s net rating — the difference between its offensive and defensive efficiency — when a player is on and off the floor.

Let’s consider combo guard Mark Smith. When the sophomore was on the floor, MU posted a 0.08 net rating, and when he was on the bench, it slid to minus-0.04, according to HoopLens’ lineup data. When we multiply the gap of 0.12 by 100 possessions, we can see that Smith’s presence is worth 12 points.

But we can go a step further. With HoopLens’ database, we can crudely gauge Smith’s impact at each end of the floor.

Quantifying Impact | Mark Smith — 2018-19

On/Off Court 2FG% 3FG% Def 2FG% Def 3FG% Net Rtg
On/Off Court 2FG% 3FG% Def 2FG% Def 3FG% Net Rtg
Mark Smith On 47.3 39.2 48 30.1 8
Mark Smith Off 48.5 34.5 51 32.7 -4
Difference -1.2 4.7 3 2.6 12
HoopLens

This simple table empowers us to draw cursory conclusions. Obviously, Smith’s jump-shooting was boon, and the dip in opponents’ shooting percentages hints at the fact he’s a stopper on the wing. But how does Smith compare to his peers? Fortunately, we can run similar calculations for each, and stack the results in rows and columns, sorting by net rating.

Take a look.

It’s also possible to measure each player’s impact on basic metrics for efficiency — KenPom’s Four Factor’s — on offense and defense. Coupling this pair of tables doesn’t answer all of our questions or impart a detailed sketch of each player’s game, but it gives an underlying sense of what each brought to the table last season.

Visualizing data not only serves as a short-hand way to check our biases at the door, but the method also contextualizes lineup decisions made by Martin and his staff.

In Smith’s case, it’s easy to grasp why he was a stalwart before being sidelined by a foot injury: He was arguably the best two-way player on the roster. Skimming Jeremiah Tilmon’s profile underscores — yet again — how his presence on the block can warp the defense and yield high-quality shots for MU’s guards.

The data confirms, to a degree, our anecdotal impressions of the practical purpose those starters served. Yet, the takeaways also matter for a reserve like freshman Xavier Pinson. No doubt, the point guard’s play improved down the stretch, but you can see that across the arc of the entire season, MU’s performance ebbed slightly when he was on the floor. The Simeon Academy product’s vision and shooting stroke are excellent tools, but his growth will be determined by cleaning up turnovers, finishing more consistently at the rim and improved off-ball defending.

Moving forward, the data can also frame discussions over who should earn more minutes. With these metrics in hand, we can rationally assess the relative strengths and weaknesses of each player — and the tradeoffs a staff might be asked to make.

Case Study: Torrence Watson vs. Javon Pickett

NCAA Basketball: Louisiana State at Missouri Denny Medley-USA TODAY Sports

During the build-up to last season, an assumption slowly baked itself into predictions about how Mizzou would fare: Torrence Watson, the highest-rated prospect in MU’s recruiting class, was a natural heir apparent to Kassius Robertson.

Even after Martin lauded Javon Pickett’s work ethic, and all but told observers to keep an eye on the freshman wing, Watson remained penciled in for heavy minutes. Yet as the season unfolded, the Belleville (Ill.) East product became entrenched in the starting five. Meanwhile, Watson’s acclimation took longer than expected. The Whitfield product’s usually pure shooting stroke was erratic. And at times, he looked overwhelmed on defense, especially tracking shooters around screens.

In early February, this was the statistical snapshot for the pair.

Pickett vs. Watson | First 22 Games

Player MPG USG% PPG RPG APG FG% 3FG% FT% eFG% TS%
Player MPG USG% PPG RPG APG FG% 3FG% FT% eFG% TS%
Javon Pickett 25.2 21.5 8.6 2.9 1.3 41.1 33.8 72.1 48.1 48.9
Torrence Watson 20.3 17.3 5.3 1.5 0.5 33.3 30.9 61.9 44 45.9

With Mark Smith lost for the season to a foot injury, MU found itself in need of a guard who could approximate his combination of deadly shooting and stingy defense. We all remember what happened next, too. Watson finally got traction offensively, while a back injury slowed down Pickett. A head-to-head comparison shows a stark juxtaposition.

Pickett vs. Watson | Final 10 Games

Player MPG USG% PPG RPG APG FG% 3FG% FT% eFG% TS%
Player MPG USG% PPG RPG APG FG% 3FG% FT% eFG% TS%
Javon Pickett 28.2 17.9 5.7 4.4 1.9 25.9 25.8 50 31.2 32
Torrence Watson 28.5 18.3 11 1.7 0.5 40.9 42.4 77.8 57.8 60.4

A close reading produces a straightforward analysis: Watson made shots, and Pickett didn’t. Fundamentally, Watson’s shot selection and mechanics didn’t undergo radical overhauls. And in Pickett’s case, his balky back is a plausible explanation for why his shooting numbers plummeted off a cliff. All the while, the pair’s respective rebounding, assists, and usage rates stayed relatively static.

Since the season ended, there’s a chance recency bias crept in. Watson’s late-season surge can be framed as the light switch finally flipping on. The pendulum may also have swung back the other way, with Watson canning 3-balls at the clip as Virginia’s Kyle Guy, Belmont’s Dylan Windler and Hofstra’s Justin Wright-Foreman.

Chances are Pickett can’t play any worse, and that Watson’s shooting might regress. As the summer passes, the question as to whether Watson or Pickett will get the bulk of the minutes might linger. The choice itself also hints at what the staff might prioritize as it constructs its rotation.

Teasing out an answer starts with evaluating the influence each has once they check into the game. Returning to the table from earlier, a head-to-head comparison gives us a loose sense of what Watson and Pickett each have to offer.

Watson’s presence helped juice MU’s offensive efficiency, a jolt that came with slight tradeoffs in rebounding. Conversely, Pickett’s presence helped the Tigers wipe the glass and less slippage on defense. None of this is particularly shocking, either, once we take into account Watson’s closing stretch.

Fleshing out their distinct profiles is also feasible using Synergy’s database, a degree of precision that also shows the margin between the rising sophomores is thin.

They predominantly operate out of spot-ups — about 4.5 possession per game — but what they do in those situations is distinct. Let’s start with Watson. Almost 76 percent of the time, he’ll launch a jumper, and practically all of them are of the catch-and-shoot variety. When Watson spaces the floor and pulls from long range, the results are promising.

Torrence Watson | Catch-and-Shoot Jumpers — 2018-19

Catch and Shoot Poss/Gm PPP Rating FGM FGA FG% eFG%
Catch and Shoot Poss/Gm PPP Rating FGM FGA FG% eFG%
Guarded 2.5 1.145 Very Good 1 2.5 36.9 54.5
Unguarded 1.2 0.974 Average 0.4 1.2 33.3 38.7
Total 3.7 1.09 Very Good 1.4 3.7 36.9 54.5
Synergy Sports

In Pickett’s case, he only took a jumper on 51.1 percent of his spot-up possessions and showed a willingness to drive the ball to the rim. (Roughly 31.1 percent of those possessions ended with a runner or at the rim.) Here’s how his possessions get parceled out.

Javon Pickett | Spot-Up Possessions - 2018-19

Spot-Up Type Poss/Gm PPP Rating FGM FGA FG% eFG%
Spot-Up Type Poss/Gm PPP Rating FGM FGA FG% eFG%
Jumper 2.3 1.027 Average 0.8 2.3 34.2 51.4
Dribble Jumper 0.4 0.692 Average 2 0.4 33.3 37.5
Runner 0.5 0.875 Good 0.2 0.5 37.5 37.5
To Rim 0.9 0.857 Below Average 0.3 0.8 40 40
Turnover/Fouled 0.4 0 Poor 0 0 - -
Total 4.5 0.847 Average 1.4 3.9 35.7 46
Synergy Sports

Pickett’s willingness to put the ball on the floor stands in stark contrast to Watson. Yet that assertiveness also dragged down his efficiency, with Pickett only shooting 39.3 percent on layups and runners.

As defenders, they each excel closing down shooters, but possession data reveals that Watson showed better promise tracking his man as they zag through screens or navigating ball screens.

Pickett vs. Watson | Defense - 2018-19

Play Type Javon Pickett Torrence Watson
Play Type Javon Pickett Torrence Watson
Spot-Up 0.8 0.836
Off Screen 1.059 0.705
Pick-and-Roll Ballhandler 1.111 0.621
Isolation 0.429 0.8
Handoff 1.059 0.857
Post-Up 1.333 1
Pick-and-Roll Roll Man 0 1.333
Overall 0.885 0.777
Synergy Sports

Before we tumble (even) farther down the rabbit hole, let’s distill all these numbers to their essence.

In Watson, MU has one of the SEC’s top returning shooters and a player who showed greater comfort and consistency as a defender. By contrast, Pickett’s shown he’ll take the fight to the defense, whether it’s as a driver, cutter or corralling a miss for a putback. And when we look at the tradeoff in its simplest terms, MU swaps 3-point shooting for rebounding when Watson takes Pickett on the floor.

Often, a coach’s preference tilts toward a player packing some more offensive punch, but Martin bucked that trend last season. Pickett’s role as a freshman is one piece of evidence that the ability to score the ball doesn’t trump all. But how much does that calculus change moving forward?

Only Martin and a cadre of assistant coaches know, seeing up close how Pickett and Watson evolve and how this iteration of the Tigers coalesces inside Mizzou Arena’s practice gym. We might not know how the metamorphosis unfolds, but at least we know where it might begin.