How To Actually Analyze Game Film Between The Suns And Grizzlies
Most people look at box scores and think they understand what happened. They miss everything that actually matters. I spent three weekends last month breaking down the last five Suns-Grizzlies meetings, frame by frame, and here is what you need to know if you are trying to do this yourself without losing your mind. The core problem with NBA game analysis is that momentum is invisible until you know where to look. Wins and losses are outcomes, not explanations. You need to track possession-level decisions, not just final scores. I start by pulling NBA's play-by-play data and Crosscheck footages from basketball-reference, then I cross-reference with cleaning the glass for advanced metrics. This takes about forty-five minutes per game if you know what you are doing. One specific headache I ran into with the Suns-Grizzlies series: the pace discrepancy between Memphis's half-court sets and Phoenix's transition offense makes raw possession count meaningless. You have to normalize by offensive possessions per game. Without that adjustment, you will incorrectly conclude that games were closer in flow than they actually were. I use pace-adjusted efficiency ratings from cleaning the glass instead of raw points per possession. This cut my analysis time from roughly two hours per game to about twenty minutes once I set up my spreadsheet properly.
What To Track In Each Game
Start with defensive assignments. Watch how Memphis handles Phoenix's screen actions, particularly the pick-and-roll between Booker or Ayton and the rolling big. Memphis switches almost everything, but they show blitz on Ayton differently than they do on Booker. That distinction matters more than anything else in these matchups. For the Suns, the critical metric is how often they attack Memphis's drop coverage in the first four seconds of the shot clock. If they settle early, they lose. I track this by noting which quarter each team starts pushing pace versus playing half-court. In the last five meetings, the Suns won three games when they took at least twelve shots in the first six seconds of the shot clock. They went 0-2 when they did not. Momentum turning points are usually small, not dramatic. A deflected pass leading to a fast break looks huge in highlights, but the actual shift happens three possessions earlier when a team changes their defensive communication. I track this by noting every time a switch was called versus a hedge, and which team called them. The team forcing more contested jump shots off late-clock looks wins more often than the stat sheet suggests.
The Turnovers That Actually Matter
Not all turnovers are equal. Live-ball turnovers lead to easy transition points. Dead-ball turnovers mostly result in a reset. I separate these in my analysis. Memphis turns the ball over more in live-ball situations because they play aggressive pressure, but those pressures create mismatches in the open floor. Phoenix avoids live-ball turnovers but struggles to generate them against Memphis's rotating defense. This creates a paradox where both teams look good defensively on paper but give up open looks anyway. Specific edge case: in Game 3 of their recent playoff series, Memphis had fewer total turnovers but gave up seven more transition points. The difference was purely in dead-ball versus live-ball split. I flagged this to my analytics group and we adjusted our model to weight live-ball turnovers at 1.8x the value of dead-ball ones. This adjustment changed our win probability estimates by about twelve percent across those five games.
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Shooting Efficiency And Variance
Three-point shooting is the most overrated metric in NBA analysis. I track effective field goal percentage and true shooting percentage instead. The Suns shoot more threes but convert at a lower rate in these matchups. Memphis shoots fewer but makes more, largely because they force higher-contention looks that still fall. This is counter-intuitive to people who only look at attempt rates. I also track free throw attempts per forty minutes, which is a better indicator of aggression and offensive design than raw free throws made. Memphis consistently gets to the line at a higher rate against Phoenix because they attack the rim more on closeouts. Phoenix's ball movement creates open threes but not drive-and-kick advantages in the same way.
Coaching Adjustments Across Five Games
Monty Williams and Taylor Jenkins make different kinds of adjustments. Williams tends to stick with his base rotation for longer stretches. Jenkins sub more frequently, especially with bench units. This creates rhythm differences that are hard to quantify but obvious on film. I track substitution timing by noting rest possessions, which is the number of consecutive possessions where one team has all starters on the floor while the other does not. A specific coaching pattern I noticed: Jenkins makes more in-game adjustment passes mid-half, changing defensive looks after just one or two plays. Williams tends to call timeouts before adjusting, which gives Memphis more time to set up. This is a subtle difference but it shows up in fourth-quarter efficiency gaps across these five games.
What The Data Actually Shows
The Suns and Grizzlies play fundamentally different styles. Phoenix relies on perimeter creation and spacing. Memphis relies on physicality and rebounding. Neither team fits the modern three-point-heavy template perfectly, which makes their matchups interesting but also makes applying league-wide analytics imperfect. I tracked fourteen tactical variables across all five games and found only five showed consistent patterns: Ayton's roll decisions, Memphis's switch communication on screens, Phoenix's transition entry speed, live-ball turnover differential, and fourth-quarter free throw rate. Everything else fluctuated enough to be noise. Beginners often try to track too many things and end up with no actionable insight. If you want to replicate this analysis, start simple. Pick one metric per game, track it for five possessions in a row, and see what repeats. Do not build a spreadsheet with seventy columns on your first attempt. I learned that the hard way during my second month of film study and wasted about six hours rewriting a template that was fundamentally flawed. The revised version took twenty minutes to set up and handled all the data correctly.
Limitations Of This Approach
No film analysis captures intangible factors like player fatigue, locker room chemistry, or referee tendencies. I have seen games where the winning team's efficiency numbers were worse in every category except one random stat. This happens. It is frustrating and it is normal. You cannot quantify everything, and pretending you can leads to false confidence in your models. For Suns-Grizzlies matchups specifically, the sample size of five games is too small to draw firm conclusions about long-term trends. Anything I am stating here applies to those five games and possibly the current roster construction. Roster changes, injuries, and coaching staff turnover will invalidate most of this within a season. Treat this as a snapshot, not a prophecy. If you want a more robust framework, look at SportVU tracking data combined with optical tracking from NBA.com. It gives you separation metrics and defensive closing speed that box scores completely miss. The learning curve is steeper, but the output is significantly more reliable for predicting future matchups between these two teams.
