How the Washington 4A Playoff Rankings Actually Work
The Washington Interscholastic Activities League (WIAA) uses a composite ranking system for its 4A classification, and understanding it matters if you want to predict who makes the state playoffs and who gets left out. The rankings combine multiple data points into a single power index that gets updated weekly through the regular season. It is not a popularity contest, and it is not purely subjective, but it is also not as mechanical as some people assume. The core formula rests on four inputs: strength of schedule, margin of victory (capped at 21 points), result against ranked opponents, and location of the game. Each component gets weighted differently, and the weighting has shifted slightly over the years. The most important thing to understand upfront is that strength of schedule carries more weight than most people give it credit for. A team that goes 7-0 against a weak schedule will often rank below a 6-1 team that played a notoriously tough slate. I spent three seasons building custom models to project playoff brackets for Washington high school football, and the first thing I learned was that margin of victory is heavily truncated. Once a team wins by 21 or more, the extra points do not count toward the ranking. This was a deliberate design choice to discourage running up the score, but it creates a strange distortion where blowing out every opponent provides diminishing returns. You can still rank highly with narrow wins, which is why some 5-2 teams end up ahead of 7-0 teams late in the season.
The second component people misunderstand is how the system handles bye weeks and games played. If one team plays nine games and another plays only seven through the same point in the season, the team with fewer games played gets adjusted so it is not unfairly penalized. The adjustment factors are published quarterly but they are not always transparent about the exact calculation method.
The Data Sources and Where They Come From
Official game results feed into the system from WIAA-sanctioned score reporting. The strength of schedule component pulls from the collective winning percentage of every opponent a team has faced. Margin of victory is calculated per game and then averaged across the season with the 21-point cap applied individually to each game before averaging. Results against ranked opponents use a quality-win bonus that rewards beating a team currently ranked in the top quarter of the classification. Home and away games carry a slight location adjustment. Road wins are worth fractionally more than home wins in the composite, though the difference is marginal. The adjustment exists to account for the inherent difficulty of playing away, but in practice it changes rankings by fractions of a point more often than whole positions. The rankings are released weekly by the WIAA on their official site, usually on Tuesday afternoons during the regular season. Third-party outlets like MaxPreps and Washington State Football Republish the numbers, but the primary source is the WIAA itself. I recommend sticking to the primary source because third-party sites occasionally typo bracket placements or misreport which week's ranking is current.
Get the Full Details
Building Your Own Ranking Model
If you want to get ahead of the official rankings, you can build a basic model using publicly available data. Here is the practical approach I used. Step one: Collect all game results from the WIAA scores page for your classification. You need date, home and away team, final score, and whether the game went to overtime. Overtime games count as a win for the winner with the actual margin, not a simplified one-point margin. Step two: Calculate strength of schedule for each team after every game. This is the cumulative winning percentage of all opponents faced. Update it after each week. Teams that play each other create a circular dependency where your opponent's SOS is partially determined by your record. The WIAA resolves this iteratively, and a simple Excel setup with a few refresh cycles converges on roughly the same numbers.
Step three: Apply the margin of victory cap. For every game, take the actual point differential and clamp it at 21 in either direction. Then average those capped margins across all games played. A team that wins 28-0, 24-3, and 14-13 has a capped average much closer to a team that wins 21-7 three times than it does to the raw scoring differential would suggest. Step four: Factor in quality wins. Assign a bonus point for each win against an opponent whose current ranking places them in the top 25 percent of the classification. This is the hardest component to replicate perfectly because it requires tracking the live ranking at the time each game was played, not the current ranking. Step five: Weight the components. The WIAA does not publish exact weights, but based on reverse-engineering years of published rankings against known inputs, the approximate breakdown is: strength of schedule at 40 percent, capped margin of victory at 30 percent, quality wins at 20 percent, and location at 10 percent. These are estimates, not official numbers, but they produce models that track within one or two ranks of the published rankings for most weeks.
Edge Cases That Break the Model
Here is a specific problem I ran into during the 2022 season that took me weeks to resolve. A mid-tier 4A team had a complicated non-conference slate that included a game against a 2A team which had itself played several 4A opponents. The SOS calculation treated the 2A team's strength as purely based on its 2A schedule, which significantly understated the actual quality of that opponent. The WIAA does not cross-classification strength adjustments into the standard formula, so any model built purely on in-classification data would overvalue that team's strength of schedule going forward. My workaround was to create a bridge score using a simple Elo-based adjustment. I ran all cross-classification games through a lightweight Elo tracker that generated a fair strength estimate for the 2A team, then fed that adjusted strength back into the SOS calculation for the 4A team. It added maybe ten minutes to the weekly process but prevented a ranking drift of three to five spots that showed up clearly by midseason. Another edge case involves teams that drop out of the rankings mid-season due to ineligibility or program violations. When a team is removed, all the rankings below it shift up, and the strength of schedule for every remaining team technically changes because one opponent is gone. The WIAA does not retroactively adjust past rankings, which means the official bracket projection can look slightly off if you are comparing early-season projected standings to late-season reality. I handle this by building a separate clean-room dataset that excludes any team known to be vacating, then running projections on that filtered set.
Using the Rankings for Playoff Prediction
The WIAA playoffs for 4A football include twelve teams per bracket region, with the top two seeds earning first-round byes. The official rankings determine seeding, but the bracket structure is region-specific. A team ranked fifth in the overall 4A classification might still be seeded eighth in its region if the regional cut is tougher. This is the part that trips up most people who are not familiar with the bracket geography. To predict playoff qualification, you need to know your region's cutoff. Historically, the twelfth seed in 4A has hovered around a ranking somewhere between eighth and fourteenth depending on how deep the region is. The Eastern region tends to be shallower than the Western region because there are fewer 4A programs spread across a larger geographic area. A team ranked eleventh overall in 4A might make the playoffs in the Eastern region but fall just short in the Western region in a given year. I track this by maintaining a rolling five-year table of twelfth-seeded records and final rankings by region. It gives you a baseline expectation. In most years, a team with a .650 or better winning percentage and a top-15 strength-of-schedule rank makes the playoffs. Teams below that threshold can still sneak in with a favorable region, but the odds drop sharply.
What the Rankings Get Wrong
The biggest limitation is that the system is reactive, not predictive. It ranks based on what has already happened, which means a team that starts slow and finishes strong will not reflect its true ability until late October. A team that starts 4-0 and then loses three straight will look far stronger than it actually is through week nine. The WIAA does not use any form of tempo-adjusted or expected-points-added metrics that modern analytics have made standard in college football. It is a traditional composite system, and that shows. Another structural issue is the cap on margin of victory. While it successfully removes incentive to run up scores, it also flattens the distinction between dominant teams and mediocre teams that happen to win close games. A team that wins six games by exactly one point and loses one by forty will often rank above a team that wins six games by an average of eighteen points and loses one by three. The data treats those two scenarios nearly identically after the cap is applied. If you want a more accurate measure of actual team strength, you should supplement the official rankings with a possession-adjusted metric. Even a simple points-per-possession model will separate dominant teams from lucky teams faster than the WIAA composite ever will. I run both in parallel and compare them each week. When the two diverge significantly, the divergence itself is usually informative about which teams are overperforming or underperforming their underlying numbers.
The location adjustment is also too blunt. A one-point boost for playing on the road is meaningful in aggregate but irrelevant for any single game prediction. The difference between playing at a natural grass field in Yakima versus a synthetic turf field in Seattle is not captured by the home-away variable at all, and neither is altitude or travel distance, which matter more than the formula acknowledges.
A Practical Week-by-Week Workflow
Here is how I actually run the model each week during the season without it consuming my entire Tuesday afternoon. I pull the WIAA score report by Wednesday morning, enter the results into a structured spreadsheet with columns for date, teams, home/away, score, and overtime flag. By Thursday afternoon I have updated SOS, capped MOV averages, quality-win counts, and the composite ranking for every team in 4A. On Friday I compare my projections against the published WIAA rankings to check for data entry errors. Any discrepancy usually traces back to a missed game or a misclassified home away designation rather than a formula issue. The entire process takes about forty-five minutes once the spreadsheet is set up, though the initial setup phase runs closer to six hours. After that, the weekly maintenance is straightforward. I do not recalculate strength of schedule more than once per week because the inputs do not change between Tuesday releases.
If you are building this from scratch and want a starting point, the WIAA publishes historical ranking archives going back several years. Downloading those and reverse-engineering the formula against known inputs is the fastest way to calibrate your own model. You will not match the WIAA exactly without their internal weighting tables, but you will get close enough that your playoff projections are more accurate than anyone picking based on win-loss records alone.
