As described in the last article, the expected wins formula is stolen from Wikipedia and thrown into an Excel spreadsheet to magically tell me who over performed and underperformed this last season. This time, that magic is being applied to the DSFL (best SFL). Because there are only six teams, I’m going to just go in whatever order I feel like writing about rather than sticking to conferences. Hopefully this isn’t too hard to follow. If it is, though, I really don’t care.
Myrtle Beach was on a mission this season following an early exit from the playoffs to Tijuana last season. The beach boys had the best season in DSFL history, allowing only 171 points. The offense struggled at times, but the team ultimately cruised to an impressive difference of +171 points. The model rewarded this by predicting the Buccs would win league-leading 11.60 games. They outperformed this expectation by .4 games, picking up 12 wins.
Minnesota, the eventual champions, had by far the most dominant offense in the DSFL. They pummeled opponents, racking up 390 points and allowing only 189. Their +204 point differential was the best in the league and it really wasn’t close. Despite the largest differential, the model only predicted they would win 11.08 games. They overperformed this expectation by .92, also winning 12 games. They eventually trounced Myrtle Beach in the championship game. While the model got the top two teams right, it did not accurately predict the champion. Congrats to the Grey Ducks. Quack quack or something.
The second place teams were Norfolk and Portland. Norfolk had the third most successful offense in the league, but their relatively poor defensive play gave them an ending differential of -30. The model predicted they would win 5.6 games. Norfolk won 6, making them by far the weaker of the second place teams. Their defense was barely better than the division losers despite their offense experiencing moderate success. Portland, on the other hand, had the second best defense in the DSFL, allowing only 139 points to be scored against them. Their offense was abysmal, though, and only scored 143. They were projected to win 6.72 games but came out of the season with 9, overperforming expectations by a full 2.28 games. This is by far the largest difference seen in either league. Barring major improvements, I would expect them to regress significantly next season.
Tijuana, unfortunately, also overperformed their expected win values. They were projected to win a measly 3.95 games on account of their -85 point differential. Their offense was only better than Portland’s and their defense gave up more than anyone. Yet they somehow managed to win five, beating the model by 1.04.
Though their gap was the smallest, Kansas City also beat the model. Their offense was okay, but the defense was by far the worst in the NFC North and was barely better than Tijuana’s to avoid worst in the league. Their differential was better than Tijuana’s, though, coming in at -56 and earning them an expected win value of 4.85. They finished the season with 5 wins, making them the closest to the expected value with a difference of only .15.
The model was far less accurate in the DSFL, possibly on account of the smaller divisions. It will be interesting to see how standings are changed with the introduction of expansion teams. It’s difficult to predict the movement of DSFL teams due to the short-lived nature of their rosters since players only tend to stick around at most two or maybe three seasons. Can Myrtle Beach and Minnesota sustain dominance, or will they fall to attrition and make way for new teams? We’ll find out next season.
Myrtle Beach was on a mission this season following an early exit from the playoffs to Tijuana last season. The beach boys had the best season in DSFL history, allowing only 171 points. The offense struggled at times, but the team ultimately cruised to an impressive difference of +171 points. The model rewarded this by predicting the Buccs would win league-leading 11.60 games. They outperformed this expectation by .4 games, picking up 12 wins.
Minnesota, the eventual champions, had by far the most dominant offense in the DSFL. They pummeled opponents, racking up 390 points and allowing only 189. Their +204 point differential was the best in the league and it really wasn’t close. Despite the largest differential, the model only predicted they would win 11.08 games. They overperformed this expectation by .92, also winning 12 games. They eventually trounced Myrtle Beach in the championship game. While the model got the top two teams right, it did not accurately predict the champion. Congrats to the Grey Ducks. Quack quack or something.
The second place teams were Norfolk and Portland. Norfolk had the third most successful offense in the league, but their relatively poor defensive play gave them an ending differential of -30. The model predicted they would win 5.6 games. Norfolk won 6, making them by far the weaker of the second place teams. Their defense was barely better than the division losers despite their offense experiencing moderate success. Portland, on the other hand, had the second best defense in the DSFL, allowing only 139 points to be scored against them. Their offense was abysmal, though, and only scored 143. They were projected to win 6.72 games but came out of the season with 9, overperforming expectations by a full 2.28 games. This is by far the largest difference seen in either league. Barring major improvements, I would expect them to regress significantly next season.
Tijuana, unfortunately, also overperformed their expected win values. They were projected to win a measly 3.95 games on account of their -85 point differential. Their offense was only better than Portland’s and their defense gave up more than anyone. Yet they somehow managed to win five, beating the model by 1.04.
Though their gap was the smallest, Kansas City also beat the model. Their offense was okay, but the defense was by far the worst in the NFC North and was barely better than Tijuana’s to avoid worst in the league. Their differential was better than Tijuana’s, though, coming in at -56 and earning them an expected win value of 4.85. They finished the season with 5 wins, making them the closest to the expected value with a difference of only .15.
The model was far less accurate in the DSFL, possibly on account of the smaller divisions. It will be interesting to see how standings are changed with the introduction of expansion teams. It’s difficult to predict the movement of DSFL teams due to the short-lived nature of their rosters since players only tend to stick around at most two or maybe three seasons. Can Myrtle Beach and Minnesota sustain dominance, or will they fall to attrition and make way for new teams? We’ll find out next season.
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