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  • Which NFL Teams Were Most and Least Affected by Injuries in 2025

    Which NFL Teams Were Most and Least Affected by Injuries in 2025

    Photo: Jeff Moreland/Icon Sportswire

    Updated 1/7/26

    The narrative about teams most affected by injury this year shifted over time as key players exited and returned, but this year’s conversation started with the 49ers.

    They already knew they were going to be missing Brandon Aiyuk for much of the season (which turned out to be the whole season), and from that point also sustained injuries to Nick Bosa, George Kittle, Fred Warner, Ben Bartch, Brock Purdy, and Ricky Pearsall, among others.

    However, the subsequent return of many of these players caused them to be surpassed in the injury accumulation department. And they ended the season pretty functional, at least offensively.

    Most Value Lost To Injury, 2025 NFL season

    Team Games Missed Total Points Missed
    Cardinals 296 266
    Commanders 222 230
    49ers 262 200
    Bills 297 190
    Dolphins 248 182
    Falcons 202 176
    Giants 278 172
    Saints 234 162
    Bengals 161 160
    Lions 326 159
    Buccaneers 250 154
    Chargers 261 148
    Steelers 279 145
    Packers 237 136
    Chiefs 199 132
    Colts 243 126
    Panthers 182 125
    Bears 336 124
    Jets 224 105
    Texans 241 101
    Ravens 183 100
    Vikings 166 99
    Titans 176 97
    Jaguars 150 94
    Browns 194 89
    Eagles 176 84
    Raiders 94 82
    Broncos 180 77
    Cowboys 236 75
    Seahawks 216 75
    Rams 145 70
    Patriots 154 59

    The value we’re measuring here is each injured player’s Total Points per game over his previous 17 games, multiplied by the number of games missed due to injury. This only counts injuries sustained since August 1.

    For those new to our work:
    Total Points is a measure of how valuable each player was to his team’s ability to score or prevent points, using Expected Points Added as the currency. Dozens of charting data points go into it: an offensive lineman blows a block, a receiver drops a pass, a defender makes an unlikely tackle, a quarterback throws into a tight window, etc. All of these successes and failures can be measured in terms of how the team’s results changed relative to what we’d expect on average, and those play-to-play values can be bundled up into a single measure of player value that can work across all positions.

    The Walking Wounded

    Kyler Murray’s injury did not affect the Cardinals’ fortunes as much as you’d think because backup Jacoby Brissett was serviceable and Murray was not lighting it up, but Murray still ranks among the most impactful injuries of the year by Total Points. Running back James Conner has been a favorite of Total Points for a little bit now because of his tackle-breaking ability, so his absence also loomed relatively large. 

    The Commanders are at the top of this list because of Jayden Daniels’ injury-plagued season. But they also missed wide receiver Terry McLaurin, running back Austin Ekeler, safety Will Harris, and defensive end Deatrich Wise.

    The Bills have more total games missed than any of the teams above them, but the names were not as big as they could have been. They are notable in that just 20 of their 190 Total Points Missed came on the offensive side of the ball, which was the second-fewest offensive points lost.

    Others Receiving Votes

    The Bengals had substantially fewer total games missed to injury than other teams with high-profile quarterback injuries. Joe Burrow’s missed time alone cost them as many points as many of the teams in the NFL suffered in total this year, but without that injury they’d rank among the most fortunate teams.

    The Lions and Bears have the most games missed due to injury in the league, but they rank just 10th and 18th respectively in Total Points missed. They were fortunate to not have their biggest names go down this year.

    Dodging Raindrops

    The Patriots, Rams, and Seahawks were the most fortunate teams in terms of health, which sheds a little light on their strong showings this year. The five teams with the fewest Total Points missed due to injury (adding in the Cowboys and Broncos) were a combined 61-23-1, compared to 39-46 from the five most injured teams.

  • A Midseason Review of the Micah Parsons Trade

    A Midseason Review of the Micah Parsons Trade

    Photo: Matthew Pearce/Icon Sportswire

    Now that the dust has settled, we can start to examine the impact of the Micah Parsons trade that happened this offseason. Both teams are experiencing significant changes with their defense this season and not only in terms of results like pressure rate and success rate, but also with how they are structured and operate as a unit. Green Bay’s decision to acquire Parsons addressed its pass rush need, while Dallas believes that Kenny Clark will help improve its run defense.  

    With the Cowboys, Micah Parsons was asked to line up all over the defensive formation but was especially effective as a stand up rusher. In 2024, The Cowboys had pressure rates of 12.9% for stand up rushers on the left and 15% for stand up rushers on the right. This year, those numbers are 13.6% and 11.5%, respectively. 

    Sacks aren’t coming as easy for the Cowboys either. Micah was often asked to mug over the A gap, and rushers from that technique sacked the quarterback 3% of the time with a staggering 21.5% pressure rate for the Cowboys. This year, mugging linebackers don’t have any sacks for the Cowboys and they are only generating pressure on 12.5% of passing plays. 

    Parsons’ versatility was a big part of how the Cowboys were able to generate pressure in 2024, and they are changing how they present themselves to offenses to try and make up for it this year. The Cowboys are lining up with stand-up edge rushers more frequently in order to try and conceal where the rush may be coming from. 

    As the Cowboys try to hunt for a comparable replacement strategy off the edge, the player they got in return, Kenny Clark, does not seem to be living up to expectations in Dallas. He has not been the difference-making run stuffer that Dallas had dreamed of, as the Cowboys defense is giving up 0.9 EPA/30 Rushes with him on the field and 0.6 EPA/30 Rushes when he’s off the field. In other words, he is not improving their run defense like the Cowboys had hoped. The last five games they’ve allowed an average of nearly 170 rushing yards allowed.

    Additionally, the Cowboys are significantly worse against the pass when Clark plays as well, giving up 9.0 EPA/30 Passes when he is on the field and -0.6 EPA/30 Passes when he’s off the field, yet the Cowboys are still playing him on 68% of passing downs. 

    Conversely, and as expected, Micah Parsons is transforming the Packers passing defense. He is a major factor against the pass, as the average EPA/Play on passing downs is nearly a full 0.1 per play better when he is on the field. The Packers’ defense also performs better as a unit against the run while he is in, allowing -3.6 EPA/60 Plays when he is on the field and -2.4 EPA/60 Plays when he isn’t.

    In tangible terms, he is more than doubling Kenny Clark’s pressure percentage as a pass rusher with a 23% pressure rate compared to Clark’s 9%.

    It remains to be seen who will ultimately prosper the most from this trade as the Cowboys are owed multiple first-round picks. But the initial returns have an immensely positive effect for Green Bay as the Packers have ultimately found their star pass rusher while maintaining success against the run. Meanwhile Dallas not only failed to improve its run defense, but is now struggling to replace the passing rushing success it once had with Parsons.

  • Study: The Impact Of The NFL’s Kickoff Rule Changes

    Study: The Impact Of The NFL’s Kickoff Rule Changes

    Through one month of the NFL season the changes to the kickoff have seemingly divided football fans. This year, the NFL decided that a touchback that first lands outside the landing zone (20 yard line back to the goal line) is brought to the 35 yard line rather than the 30 yard line like in 2024.

    Is one minor change producing higher return rates, better average field position, and shorter kick hang times? Kickers are even kicking the ball in different ways to try and combat excelling return rates. We’ll look back and compare to last season to see just how much things have changed. 

    Kick Type

    The 2025 season has featured the highest use of non-normal kick types on kickoffs that we have seen. At SIS, we track kick types, and we classify a “normal” kickoff as a kick with a typical trajectory having a hang time between 3.3 – 3.8 seconds. 

    Through the first four weeks of the season, there has been a non-normal kick on 28% of kickoffs (not including squib or onside kicks). Comparatively in 2024, there was a non-normal kick on only 7% of kickoffs. Kickers are actively reducing their hang time and changing the way they kick the ball to either add or take away spin. 

    Hang time

    The increased frequency of line drive kicks is manifesting itself in the hang time as well. During the 2024 season, the average hang time on NFL kicks was 3.86 seconds. Through the first month this season, the average hang time is down to 3.37 which is about half a second shorter than last season. The difference between 2024 and 2025 gives kick returners more opportunity to create explosive returns with increased return lanes available. 

    The average hang time the two seasons before the Dynamic Kickoff was implemented was 4.00. A big part of this is the kicker allowing the ball to hit the ground before it can be caught by the returner, which in turn gives the kick coverage more time to cover potential return lanes. 

    Even if you look within a given kick type, the kicks are spending less time in the air. On non-line-drives, the average hang time is about a third of a second shorter than it was a year ago.

    Returns

    The decreased hang time on kickoffs is encouraging teams to take a chance and return the ball at a much higher rate than 2024. Last season 65% of kickoffs resulted in touchbacks, whereas so far in 2025 only 17% of kickoffs have resulted in touchbacks. 

    However, the results of returned kicks have been basically the same. The average length of returns are slightly down from last year (27 yards in 2024 and 25 in 2025). That’s made up by the average start of the return increasing from the 2 yard line to the 4 yard line between seasons. With touchbacks outside the landing zone being brought out to the 35, kickers are incentivized to make sure the kick lands in the landing zone thus producing returns at a significantly higher rate and those returns starting slightly farther from the end zone. 

    If you incorporate the touchbacks and returns into a single average, the resulting field position has nudged forward a bit, but not by much. From 2016 to 2024, teams started their drive somewhere between the 23 and 24 yard line on average every year. In 2025 so far, the average is the 26 yard line.

    Injuries

    One of the big talking points around the use of the dynamic kickoff was the potential for getting more action without increasing the rate of injury. Through four weeks of kickoffs, the per-play injury rate has been consistent with previous years, but the dramatic spike in returns leads to more total injuries. 

    SIS has charted more injury events on kickoffs through Week 4 than the previous three years combined. But those have come on a similar increase in returns. If you look per-return, the injury rate is a bit higher than 2023-24 but right in line with 2021-22. 

    Findings

    It’s still early, but a simple change of moving a touchback from the 30 yard line to the 35 yard line is producing some consequential effects in how the game looks, but it may not be changing as much as people initially thought. Contrary to popular belief, the average starting field position for all returned kicks is the 29 yard line, which is the same for both 2024 and 2025. Injuries are up, but that’s just a factor of there being more football happening. 

    One thing we know for sure: return rates are dramatically increasing and kickers are kicking the ball with less hang time in order to keep the length of return down. So far it’s been working.

  • What Liam Coen Means When He Says It’s 100% A Go

    What Liam Coen Means When He Says It’s 100% A Go

    The Jaguars seemed to pull defeat from the jaws of victory against the Bengals on Sunday, enough so that head coach Liam Coen had to answer for a particular fourth down play late in the game.

    With just under 4 minutes left and a 3-point lead, they chose to go for a 4th-and-5 from the Bengals’ 7 yard line instead of lining up for a chip-shot field goal. They failed to convert, and the Bengals proceeded to march 92 yards down the field and score the winning touchdown with barely any time left on the clock.

    Asked about it afterward, Coen said, “In all analytics, in all data, it’s 100% a go.” And in response to that, I watched a TV personality question that statement, saying you have to feel out the situation.

    But honestly, we might not even need to bring analytics into play.

    Normally, there’s a lot of Monday-morning-quarterbacking after having seen the result, and people browbeat the decision-maker in part because of the bad result. But in this spot, the eventual result presents the exact reason why going for it is a good decision! If we knew the Bengals were going to be able to go down the field and score a touchdown with no time left, obviously the Jaguars should be pushing for a touchdown in that spot.

    But let’s take a look at “the analytics” and see how we might have evaluated that question.

    SIS has a win probability model that is trained on the last few years of basic game state information, and it can also incorporate a measure of recent team strength. For fourth down decision-making purposes, we consider the three possible choices (punt, kick, and go for it) and pit them against each other based on historical performance.

    I’ll start with the top-level result: we also would have this as a “go”. I’ll explain in a bit why there could be some slight caveats there.

    Field goal: 77% win probability

    Based on recent league history, we estimate that a field goal from this spot has a 98% chance of bringing your lead to 6. The result is likely to be the Bengals starting their upcoming drive down by 6 at around their own 30, which puts the Jaguars at about a 77% chance to win according to our model, all else equal.

    The Jaguars might think their kicker is better or worse than that average, but there’s really only downside in that wiggle room, given how short the kick is. So that’s not something that makes the decision any more difficult to make.

    Would that number be meaningfully different if we incorporated the strength of the opponent? Not really, because by this point in the game there isn’t that much time for the strength or weakness of a team to exert itself. Anything can happen in a one-drive sample.

    Go for it: 84% win probability

    With the go-for-it option, there are some more considerations.

    If the Jaguars moved the chains, recent data suggests a better-than-even chance of making it into the end zone on that play, but even if they didn’t, we’re looking at a 94% chance of winning. You’re still very likely to score a touchdown, you could kick a field goal and burn a bunch of clock or timeouts, or you could burn clock and bury the Bengals in the shadow of their own goal post.

    If they don’t make it, they’ve still set the Bengals up for a long drive to make. They could force overtime with a field goal (producing a ~50/50 situation), or drive the full distance for a winning score in regulation. In that situation, the Jaguars have a 74% chance of winning.

    We estimate the success rate for a 4th-and-5 near the goal line to be 45%. Right around a coin flip, so right around halfway between the success and failure situations.

    The result is that we estimate a pretty meaningful advantage to going for it over kicking the field goal. Of course, 7 percent is a much less impactful difference late in the fourth quarter than it is in the first, so we wouldn’t have this as a 100% no-brainer, but it’s a decent chunk of value.

    Is the answer that simple?

    When we talk about sports analytics, it’s important to acknowledge two things: “analytics” isn’t some single methodology that produces a monolithic conclusion, and analytical research is not the final answer to any question.

    Our model is going to produce a different win probability for all of these situations than plenty of other models. We’re all using similar inputs, but we’re employing them differently, making different underlying assumptions, and deploying different modeling approaches. If our model is +/- 5 percent different from another model in most situations (not that much in the scheme of things), then it’s possible that another model thinks that there’s a slight lean in favor of a field goal in this spot.

    And what about factors the model doesn’t consider? Were the Jaguars playing below-average football at that time, and therefore could be assumed to have a lower expected success rate on the fourth down try? With our model, that expected success rate would need to be as low as 15% to flip the decision. That’s a tough sell on its own, but maybe a couple specific features of this situation conspire to tip the scales.

    That’s why you want a give and take between the team’s model and the decision makers on the ground. The model is going to consider a whole bunch of factors and operationalize years of relevant history, but it might not be reflecting that the Bengals’ quarterback got hurt earlier in the game, or that the Jaguars’ passing game was generating under 5 yards per attempt in the second half. NFL rules are fairly constrained on what tech can and cannot be employed during the game, so there isn’t much leeway to do in-game model adjustments.

    All of these recommendations should have a fudge factor, which is something we employ in our own fourth down reports. We’ll have some very strong recommendations but given the combination of the model’s wiggle room and the specific context of the game, there are a ton of situations in the squishy middle. It’s in that squishy middle where teams can use some of this context to their advantage; they just have to be conscious of treating that new information with the appropriate number of grains of salt.

  • A New Expected Points Model

    A New Expected Points Model

    In the dynamic world of football, there are an infinite number of variables to consider when analyzing the game. Expected Points offers a lens through which to view team performance, moving beyond simple yardage gains. By accounting for critical game state variables, Expected Points provides a robust baseline for evaluating how well a team moves (or stops) the ball.

    As an analytics service, we have our own Expected Points model, as many services do, that takes game state variables and quantifies the amount of points a team should score on the play. However, by recently digging into our original model, we found some gaps that we wanted to address when comparing the actual scoring results of the game. Before addressing those changes, let’s dive into how the original model was built.

    The Old Model

    Our previous model used down, to go distance, distance to the end zone, and whether or not the offense is the home team. Usually, the first three listed are the core of all Expected Points models, but we also added the binary “home offense” feature to add a little more context.

    Although effective, we found a phenomenon that our model was less calibrated at the end of halves, especially at the end of games. Also, we found a substantial difference between the actual results and our model in 4th quarters as to whether or not the offensive team was losing. To get to the root cause, we needed to dive deeper into the scoring environment at these times.

    Time Left vs. Scoring Percentage by the End of the Half

    In the graph above, we can see that the rate of scoring decreases as available time in the half decreases in the NFL (time left = 0 on the left side of the chart). Intuitive, yes, but we also see the severity changes given the times. 

    In the 4th quarter, we see scoring begin to decrease sharper at the 15 minute mark (beginning of the 4th quarter) and then decrease more and more sharply at the 2 minute intervals outlined above. 

    This also occurs in the first half and in overtime, but the decline starts much later. The shape in the last 4 minutes of the first half is mostly similar to the shape of the 8-10 minute mark in the second half. This same trend exists on the college side as well.

    NFL Model Calibration – Pre Changes

    In the graph above highlighting the NFL calibration before the changes, there is a distinct gap in expected scoring and actual scoring on average in the 4th quarter in all 3 scoring margin buckets. The model underpredicts scoring when a team is losing (as those teams are often hurrying to catch up), and overpredicts scoring when a team is winning and tied (as those teams are often slowing things down), but tied is a much lesser degree. At the end of the first half, there is a slight deviation inside 4 minutes, but not nearly as severe as the end of the game.

    CFB Model Calibration – Pre Changes

    From a college perspective, the model shows more deviance than the NFL. There is still an effect at the same time ranges that were previously highlighted, but there are bigger gaps in the winning and losing phases. The larger gaps in the college model might be attributed to larger gaps in team quality, which we are not addressing in this model. For the purpose of this re-work, the time and lead theories still apply here.

    After reviewing this data, we concluded that both end of half situations combined with the lead type at the end of the game factor into a pace of play component that has an effect on expected scoring in a game, and that pace takes effect at the end of the first half and in the 4th quarter.

    This is not to be confused with the rating of the teams with the lead component, which we did not want to build into the Expected Points model. This model is centered around the state of the game, factoring in average outcomes against the level at which teams are playing (NFL or college). A model that incorporates team rating is more complex and something that we did not want to attempt at this time. The general trend of “good teams are winning more” is reversed once the lack of time to score comes into play. This specific state of the game factor is what we are trying to account for.

    If we did incorporate team ratings, this would help the college model more given the larger gaps in winning and losing.

    The New Model

    To factor in pace, three new features were created for the model. These new features are described as follows:

    1. Quarter Grouping:
      1. 10 minutes and under to go in the 4th quarter
      2. 2 minutes and under to go in the 2nd quarter
      3. All other time situations
    2. Time Left in the Quarter in Minutes:
      1. Counting down from 10 by 2s (10,8,6,4,2) for the 4th quarter and only a 2 for the 2 minute mark and under in the 2nd quarter 
      2. All other times are labeled as a 15 to be the catch all
    3. Offensive Team Lead Grouping:
      1. Losing (<10 minutes left in the 4th quarter)
      2. Winning (<10 minutes left in the 4th quarter)
      3. Tied (<10 minutes left in the 4th quarter)
      4. All other cases (>10 minutes left in the 4th quarter)

    The time features were engineered this way strictly to look at the specific time periods under consideration. This is a proxy for the pace of play at the end of halves where a team may operate differently when under a time crunch and if they are winning or losing. The goal isn’t to try and find the difference in play at all times of the game, which is why the time groupings were created instead to only capture the times when the game context imposes a pace on a team.

    NFL Model Calibration – Post Changes

    CFB Model Calibration – Post Changes

    The calibrations are now more aligned at the end of halves and follow the pattern of actual scoring. The college model still sees larger disparities in the winning and losing phases with over-predicting scoring when losing and under-predicting scoring when winning. However, the end of game situations are much better. The NFL model adjusted smoothly to the actual results at the end of halves as well, especially in higher expected scoring environments when a team is losing.

    The goal of improving our models incorporating pace at the end of halves given the lead situation has been met here. The calibration to actual scoring on average has improved in both NFL and college. With this improvement at the base level of evaluation, we can now assess EPA metrics more accurately when it comes to teams as well as our Total Points metric to evaluate players.

  • 2025 SIS Preseason All-Sun Belt Team

    2025 SIS Preseason All-Sun Belt Team

    It’s time once again to announce our SIS College Football Preseason All-Sun Belt Team. We used our all-encompassing player value stat, Total Points, along with other metrics and our scouting work as leading references in putting together our selections.

    A brief explanation of Total Points:

    Total Points takes nearly everything that SIS measures about a play and uses it to evaluate each player on a scale that allows you to compare them more easily. It’s always useful to be able to understand the different ways in which players can be valuable. Does he break a lot of tackles? Does he get a lot of yards after the catch? Does he make the best out of a poor offensive line? Does he get more pressures than expected? Does he break up a lot of passes? Total Points offers the opportunity to take all of those elements and get a quick picture of how well a player is performing overall.

    You can learn more about Total Points and the statistics referenced within this piece here.

    Here are our selections:

    1st Team Offense

    Position Name School
    QB Jaylen Raynor Arkansas State
    RB Kentrel Bullock South Alabama
    WR Ted Hurst Georgia State
    WR Corey Rucker Arkansas State
    WR Adrian Norton Marshall
    TE Kyirin Heath Southern Miss
    T Dorion Strawn Texas State
    T Nick Del Grande Coastal Carolina
    G Pichon Wimbley Georgia Southern
    G Kenton Jerido South Alabama
    C Thomas Johnson Coastal Carolina

    1st Team Defense

    Position Name School
    DT Immanuel Bush James Madison
    DT Chris Boti Arkansas State
    EDGE Jo’Laison Landry Texas State
    EDGE Bryan Whitehead II Arkansas State
    LB Jordan Stringer Troy
    LB Jaden Dugger Louisiana
    CB David Godsey Jr. UL-Monroe
    CB Josh Moten Southern Miss
    S Jacob Thomas James Madison
    S Carl Fauntroy Jr. UL-Monroe
    S Justin Meyers Georgia Southern

    1st Team Specialists

    Position Name School
    K Clune Van Andel Arkansas State
    P Alex Smith Georgia Southern
    Returner Robert Williams Louisiana

    Dorion Strawn made our Preseason All-American 1st team after a strong 2024 season that saw him finish 8th among FBS tackles with 39 Total Points. As with many conferences these days, a large amount of the 2024 All-Sun Belt team either graduated or transferred, leading to a balanced conference with 8 teams represented on the offense and 8 teams represented on the defense.

    Nick Del Grande was Top-20 last season among FBS tackles in Total Points Above Average Per Snap, and Jo’Laison Landry could be in for a big season after earning 12 Total Points Above Average last season in far fewer snaps than most of his competition.

    Kentel Bullock has the most Total Points among returning Sun Belt running backs, despite splitting the carries last season, and will look to lead the South Alabama offense this season. Josh Moten had 16 Total Points Above Average last season which is the most among returning Sun Belt defenders and will look to lock down the Southern Miss backend.

  • 2025 SIS Preseason All-Mountain West & Pac-12 Team

    2025 SIS Preseason All-Mountain West & Pac-12 Team

    It’s time once again to announce our SIS College Football Preseason All-Mountain West & Pac 12 Team. We used our all-encompassing player value stat, Total Points, along with other metrics and our scouting work as leading references in putting together our selections.

    A brief explanation of Total Points:

    Total Points takes nearly everything that SIS measures about a play and uses it to evaluate each player on a scale that allows you to compare them more easily. It’s always useful to be able to understand the different ways in which players can be valuable. Does he break a lot of tackles? Does he get a lot of yards after the catch? Does he make the best out of a poor offensive line? Does he get more pressures than expected? Does he break up a lot of passes? Total Points offers the opportunity to take all of those elements and get a quick picture of how well a player is performing overall.

    You can learn more about Total Points and the statistics referenced within this piece here.

    Here are our selections:

    1st Team Offense

    Position Name School
    QB Maddux Madsen Boise State
    RB Floyd Chalk IV San Jose State
    RB Anthony Hankerson Oregon State
    WR Cade Harris Air Force
    WR Nick Cenacle Hawaii
    TE Matt Lauter Boise State
    T Kage Casey Boise State
    T Caden Barnett Wyoming
    G Richard Pierce New Mexico
    G AJ Vaipulu Washington State
    C Costen Cooley Air Force

    1st Team Defense

    Position Name School
    DT Payton Zdroik Air Force
    DT Ben Florentine Wyoming
    EDGE Nikko Taylor Oregon State
    EDGE Trey White San Diego State
    LB Tano Letuli San Diego State
    LB Blake Fletcher Air Force
    CB Al’zillion Hamilton Fresno State
    CB Chris Johnson San Diego State
    CB Dylan Phelps Colorado State
    S Skyler Thomas Oregon State
    S Ayden Hector Colorado State

    1st Team Specialists

    Position Name School
    K Gabriel Plascencia San Diego State
    P Luke Freer Air Force
    Returner Abraham Williams New Mexico

    The college football world is just one year away from the Pac-12 poaching many of the Mountain West teams to rebuild the once great conference, so that is the reasoning for combining these two conferences to create one preseason All-Conference Team. 

    Maddux Madsen headlines the offensive team after helping lead the Boise State Broncos to the first edition of the 12-Team College Football Playoff. He is joined by his fellow teammates Kage Casey and Matt Lauter. Both Cade Harris and Floyd Chalk IV from Air Force and San Jose State return as two of the most dynamic playmakers in the conference heading into this upcoming season. 

    Defensively, Fresno State’s Al’Zillion Hamilton headlines the defensive team with 56 Total Points from the 2024 season which puts him in the Top 5 of all returning cornerbacks in the nation. San Diego State looks to have a strong defense as they lead the way with 3 players selected in EDGE Trey White, LB Tano Letuli, and CB Chris Johnson.  

    Abraham Williams is looking to make an impact as a returner for New Mexico this season after showing his playmaking ability in the FCS ranks at both Idaho and Weber State. 

     

  • 2025 SIS Preseason All-MAC Team

    2025 SIS Preseason All-MAC Team

    It’s time once again to announce our SIS College Football Preseason All-MAC Team. We used our all-encompassing player value stat, Total Points, along with other metrics and our scouting work as leading references in putting together our selections.

    A brief explanation of Total Points:

    Total Points takes nearly everything that SIS measures about a play and uses it to evaluate each player on a scale that allows you to compare them more easily. It’s always useful to be able to understand the different ways in which players can be valuable. Does he break a lot of tackles? Does he get a lot of yards after the catch? Does he make the best out of a poor offensive line? Does he get more pressures than expected? Does he break up a lot of passes? Total Points offers the opportunity to take all of those elements and get a quick picture of how well a player is performing overall.

    You can learn more about Total Points and the statistics referenced within this piece here.

    Here are our selections:

    1st Team Offense

    Position Name School
    QB Parker Navarro Ohio
    RB Al-Jay Henderson Buffalo
    WR Junior Vandeross III Toledo
    WR Terry Lockett Jr. Eastern Michigan
    WR Victor Snow Buffalo
    TE Blake Bosma Western Michigan
    T Evan Malcore NIU
    T Davion Witherspoon Ohio
    G Benjamin Roy Jr. UMASS
    G Mickey Rewolinski Eastern Michigan
    C Alex Padgett Bowling Green

    1st Team Defense

    Position Name School
    DT Nasir Washington Miami (OH)
    DT Darin Conley Ball State
    EDGE Louce Julien Toledo
    EDGE Roy Williams Northern Illinois
    LB Red Murdock Buffalo
    LB Dakota Cochran Central Michigan
    CB Avery Smith Toledo
    CB Tank Pearson Ohio
    S Eli Blakely Miami (OH)
    S Silas Walters Miami (OH)
    S Braden Awls Toledo

    1st Team Specialists

    Position Name School
    K Dom Dzioban Miami (OH)
    P John Henderson Bowling Green
    Returner Bryson Hammer Toledo

    Ohio’s Parker Navarro leads the way for the offensive selections and returns to the Bobcats as one of the conference’s most efficient QB’s in 2024. Al-Jay Henderson looks to build upon his impressive 1,000 campaign for Buffalo and is joined by his teammate at WR, Victor Snow. Western Michigan TE Blake Bosma’s 23 Total Points puts him in the top 10 of returning TE’s in the country. The offensive line is bookended by OT’s Evan Malcore and Davion Washington who are both in the top 30 for Total Points Rank among all returning OT’s in the nation. 

    Defensively, Red Murdock returns to Buffalo after racking up 54 Total Points in 2024, which puts him in the top 5 of all returning LB’s in the country. Miami (OH) returns one the best safety duos with Eli Blakey and Silas Walters. Toledo also has a secondary duo of their own with S Braden Awls and CB Avery Smith as well. 

    Miami (OH) specialist Dom Dzioban had an impressive 2024 season after making the transition from punter to field goal kicker, converting on 86% of his attempts last season.

  • 2025 SIS Preseason All-Conference USA & Independents Team

    2025 SIS Preseason All-Conference USA & Independents Team

    It’s time once again to announce our SIS College Football Preseason All-CUSA Team, plus Independents. We used our all-encompassing player value stat, Total Points, along with other metrics and our scouting work as leading references in putting together our selections.

    A brief explanation of Total Points:

    Total Points takes nearly everything that SIS measures about a play and uses it to evaluate each player on a scale that allows you to compare them more easily. It’s always useful to be able to understand the different ways in which players can be valuable. Does he break a lot of tackles? Does he get a lot of yards after the catch? Does he make the best out of a poor offensive line? Does he get more pressures than expected? Does he break up a lot of passes? Total Points offers the opportunity to take all of those elements and get a quick picture of how well a player is performing overall.

    You can learn more about Total Points and the statistics referenced within this piece here.

    Here are our selections:

    1st Team Offense

    Position Name School
    QB Maverick McIvor Western Kentucky
    RB Jeremiyah Love Notre Dame
    RB Jadarian Price Notre Dame
    WR Jaden Greathouse Notre Dame
    WR Malachi Fields Notre Dame
    TE Louis Hansen UConn
    T Aamil Wagner Notre Dame
    T Marshall Jackson Western Kentucky
    G James Dawn II Sam Houston State
    G Billy Schrauth Notre Dame
    C Aaron Fenimore Liberty

    1st Team Defense

    Position Name School
    DT Donovan Hinish Notre Dame
    DT Jared Dawson Notre Dame
    EDGE Junior Tuihalamaka Notre Dame
    EDGE Jawaun Campbell Jacksonville State
    LB Jaylen Sneed Notre Dame
    LB TyQuan King UConn
    LB Kolbe Fields Louisiana Tech
    CB Christian Gray Notre Dame
    CB Leonard Moore Notre Dame
    S Adon Shuler Notre Dame
    S CJ Brown Sam Houston State

    1st Team Specialists 

    Position Name School
    K Chris Freeman UConn
    P James Rendell Notre Dame
    Returner Kam Thomas UTEP

    Figuring out where to put the Independent teams was a struggle. With only 2 remaining Independent teams, it does not make sense to give them their own team, but it is also tough to take spots away from the true conference teams as well. For the preseason teams, we decided to put Notre Dame and UConn with the C-USA teams as it is a smaller conference and the majority of players from the 2024 All-CUSA have either graduated or transferred out. That being said, there is a strong possibility that Notre Dame and UConn will not be on any All-Conference teams when the postseason teams are announced.

     Jeremiyah Love, Christian Gray, Aamil Wagner, and Adon Shuler all made our Preseason All-American 1st or 2nd team. Notre Dame enters the season as a true title contender and as a result, their players are all over this team. Their offensive skill positions, defensive line, and secondary in particular are major areas of strength.

    James Dawn II and Aaron Fenimore were excellent last year with Dawn II posting a Blown Block Rate of just 0.2% and Fenimore finishing Top-10 in the FBS in Total Points among centers. TyQuan King had 16 Total Points Above Average last season which is the top mark among returning C-USA defenders.

  • 2025 SIS Preseason All-AAC Team

    2025 SIS Preseason All-AAC Team

    It’s time once again to announce our SIS College Football Preseason All-AAC Team. We used our all-encompassing player value stat, Total Points, along with other metrics and our scouting work as leading references in putting together our selections.

    A brief explanation of Total Points:

    Total Points takes nearly everything that SIS measures about a play and uses it to evaluate each player on a scale that allows you to compare them more easily. It’s always useful to be able to understand the different ways in which players can be valuable. Does he break a lot of tackles? Does he get a lot of yards after the catch? Does he make the best out of a poor offensive line? Does he get more pressures than expected? Does he break up a lot of passes? Total Points offers the opportunity to take all of those elements and get a quick picture of how well a player is performing overall.

    You can learn more about Total Points and the statistics referenced within this piece here.

    Here are our selections:

    1st Team Offense 

    Position Name School
    QB Brendon Lewis Memphis
    RB Eli Heidenreich Navy
    RB Noah Short Army
    WR Easton Messer Florida Atlantic
    WR Anthony Smith East Carolina
    TE Houston Thomas UTSA
    T Chris Adams Memphis
    T Jimarion McCrimon East Carolina
    G Paolo Gennarelli Army
    G Ben Purvis Navy
    C Brady Small Army

     

    1st Team Defense

    Position Name School
    DT Landon Robinson Navy
    DT Blake Boenisch Rice
    EDGE Kameron Hamilton Tulane
    EDGE Mo Westmoreland Tulane
    LB Sam Brumfield Memphis
    LB Chavez Brown South Florida
    CB David Fisher North Texas
    CB Elijah Green Tulsa
    S Bailey Despanie Tulane
    S Josh Baka UAB
    S Casey Larkin Army

     

    1st Team Specialists

    Position Name School
    K Jonah Delange UAB
    P Dante Atton Temple
    Returner Quinton Jackson Rice

    Army offensive linemen Paolo Gennarelli and Brady Small made our Preseason All-American 1st team. Despite not being a traditional power conference, there are some major playmakers in the American Conference as Brendon Lewis, Eli Heidenreich, and Landon Robinson all were Top-20 in the FBS at their respective positions in Total Points last season.

    Jimarion McCrimon, Blake Boenisch, and Chavez Brown are in line for bigger roles this season after finishing Top-25 in Total Points Above Average Per Snap last season at their respective positions.

    The majority of players from the 2024 All-AAC team have either graduated or transferred, and there is plenty of room for players to earn their spot on our postseason team. The balance of the conference is evident with 6 different schools represented on the offensive side and 9 different schools represented on the defensive side.