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Football Power Index

The Football Power Index (FPI) is a system developed by to measure the relative strength of teams in the (NFL) and , forecasting their performance in upcoming games and the remainder of the season through simulations based on offensive, defensive, and special teams efficiencies. Introduced for in 2014 and expanded to the NFL in 2015, FPI uses expected points added (EPA) per play from historical and current game data, adjusted for factors like opponent strength, home-field advantage, travel, rest days, and altitude, to generate ratings expressed as points above or below the league average on a neutral field. In its methodology, preseason FPI ratings incorporate prior season performances, returning production (such as starters and recruiting classes for teams), coaching changes, and quarterback stability, while in-season updates employ Bayesian to iteratively refine projections after each game. Unlike retrospective rankings that evaluate past results, FPI emphasizes forward-looking predictions, simulating each team's remaining schedule 10,000 to 20,000 times (depending on the variant) to estimate win probabilities, playoff chances, and season outcomes, with historical accuracy rates around 70-77% for game predictions. The system accounts for contextual nuances, such as reduced offensive efficiency in later-season games or penalties for injuries, making it a tool for fans, analysts, and broadcasters to assess team potential beyond win-loss records. While separate implementations exist for and —reflecting differences in data availability and schedule structures—both share the core goal of providing objective, data-driven insights into competitive balance.

Introduction

Definition and Purpose

The Football Power Index (FPI) is a predictive developed by to quantify team strength in . It measures a team's true strength on a net points scale, representing the expected point margin against an average opponent on a neutral field. The primary purpose of FPI is to forecast future performance rather than retrospectively rank past results, enabling projections of game outcomes, season records, playoff probabilities, and championship odds through simulated scenarios. This forward-looking approach aims to provide a reliable predictor of team success for the remainder of the season, incorporating factors like to adjust for varying levels of competition. FPI is applied to both NFL and college football, generating win probabilities for individual matchups, projected win-loss records, and overall team rankings to guide fans, analysts, and bettors in evaluating season trajectories.

History and Development

The Football Power Index (FPI) originated as a predictive analytics tool developed by ESPN's Stats & Information Group in 2013, initially for college football, to measure team strength and forecast game and season outcomes using advanced metrics such as expected points added (EPA). This system built upon ESPN's earlier innovations in football analytics, including the EPA metric introduced in 2011, which quantifies the value of each play in terms of scoring probability. Key contributors included analysts like Brad Edwards, who helped refine the model's integration of historical data and simulations. The college FPI made its preseason debut in 2014, incorporating factors such as prior performance, returning starters, recruiting rankings, and coaching tenure to generate forward-looking ratings. ESPN extended the FPI framework to the in 2015, adapting it through the production analytics team led by Brian Burke, Zach Bradshaw, and Alok Pattani, who drew on millions of play-by-play data points dating back to 2006 to emphasize predictive efficiency over past rankings. Early versions from 2015 to 2017 primarily relied on EPA per play for offensive, defensive, and special teams components, with ongoing in-season updates to reflect performance trends. By 2016, the college FPI had evolved to utilize four seasons of historical data, Bayesian regression for adjustments, and opponent-specific factors like game site and altitude, while the NFL variant incorporated impact via aging curves to account for positional . These refinements enabled 10,000 simulations per schedule to project win probabilities and season outcomes. Key milestones include the 2016 college FPI season, where the favored team won 75% of games over a 10-year historical backtest, rising to 77% in the most recent four seasons, demonstrating improved predictive accuracy. By the , the college FPI had scaled simulations to 20,000 per projection, while the NFL version uses 10,000 simulations, enhancing granularity for playoff scenarios amid format changes like the NCAA's 12-team expansion. As of 2025, FPI continues weekly real-time adjustments using current season data, maintaining its role as ESPN's core tool for both college and professional football forecasting without major structural overhauls reported in recent years.

Key Characteristics

Core Metrics

The Football Power Index (FPI) rating for a team is derived from the sum of three primary components: the , Defensive Rating (DEF), and Special Teams Rating (ST). Each component quantifies a team's efficiency in expected points added (EPA) per play against an average opponent on a neutral field, providing a measure of contribution to the net scoring margin. The evaluates a team's scoring efficiency by assessing how effectively its plays advance the ball and contribute to points scored, adjusted for the strength of opposing defenses. This metric captures the overall potency of the offense in generating EPA, focusing on factors such as play success rates and situational performance rather than raw yardage or touchdowns alone. The Defensive Rating measures a team's ability to limit opponent scoring, based on EPA allowed per defensive play and adjusted for the quality of opposing offenses. It highlights efficiency in preventing points through stops, turnovers, and field position control, ensuring comparisons account for schedule difficulty. The Special Teams Rating accounts for the impact of kicking, punting, and return units on field position and scoring opportunities, quantified via EPA per special teams play. This component emphasizes advantages gained or lost through elements like coverage, kickoff returns, and accuracy, which can significantly influence game outcomes beyond offensive and defensive plays. The overall FPI value, expressed in points, indicates the expected win margin for a team against an average opponent on a neutral site; for instance, an FPI of +7.7 suggests a 7.7-point . When projecting actual games, FPI incorporates home-field advantage, typically valued at 2.5 to 3 points in favor of the home team.

Simulation and Projections

The Football Power Index (FPI) generates forward-looking predictions by running extensive simulations of each team's remaining schedule, incorporating probabilistic outcomes derived from FPI differentials between opponents. For the NFL, these simulations typically involve 10,000 s per team, while uses 20,000 simulations to account for the larger number of teams and more complex playoff structure. Each introduces in game results, modeled as a around the expected point differential from FPI ratings, to simulate variability in performance and produce a of possible outcomes. The primary outputs of these simulations include projected win-loss records, game-specific win probabilities, and postseason advancement chances. For individual games, FPI calculates win probabilities based on the rating differential, adjusted for home-field advantage, rest days, and travel—such as a 65% chance for a higher-rated team to defeat a lower-rated opponent in a neutral-site matchup. Season projections aggregate these to estimate overall records, division titles, playoff probabilities, and odds; for example, in the , outputs might show a team with an 11.5-5.5 projected record, 80% playoff odds, and 15% chances. In , similar projections extend to conference championships, , and national title odds, with win-out percentages providing insight into upset potential. Simulations incorporate key factors to enhance realism, including (which can swing projected wins by about one game over a full season), bye weeks or rest advantages (valued at roughly one point per game), and injuries through adjusted efficiency ratings—particularly for quarterbacks, where backups reduce a team's FPI by several points based on historical performance data. Travel distance and environmental effects, like altitude, also factor in, adding or subtracting fractional points to differentials. For conference standings, especially in , simulations account for clustering effects, where identical FPI ratings can yield divergent projections due to intra-division matchups and scenarios. As of November 9, 2025 in the season, top-ranked teams like the hold a No. 1 FPI rating with a projected playoff probability of 75.8%, a 25.5% chance to win the , and 18.9% odds, reflecting their strong offensive and defensive efficiencies despite a 5-4 record.

Computation Method

Mathematical Foundation

The Football Power Index (FPI) employs a Bayesian framework to update team ratings on a weekly basis, integrating beliefs with new from game outcomes. Initial priors are established from preseason expectations derived from the previous season's performance data, providing a starting point for each team's offensive, defensive, and special teams components. As the season progresses, these priors are updated using Bayesian regression, which combines historical trends with current game results to refine ratings iteratively after each matchup. This approach allows FPI to evolve dynamically while maintaining stability against noise from individual games. At the core of FPI's calculations is the integration of Expected Points Added (EPA) per play, a metric that quantifies the efficiency of offensive, defensive, and special teams units by measuring the change in expected points resulting from each play, adjusted for context such as down, distance, and field position. EPA values are aggregated across plays to produce game-level margins, which are then opponent-adjusted to account for the strength of competition faced. The resulting team strength rating, expressed as FPI, represents the predicted point differential against an average opponent on a neutral field. Game predictions under FPI incorporate these ratings to estimate win probabilities, using a model based on the difference in team strengths adjusted for factors like home-field advantage, travel, and rest. The model is calibrated to historical outcomes, where larger differences correspond to higher certainty of victory. modeling is inherent to the Bayesian structure, particularly in handling small sample sizes early in the season. Variance estimates are derived from the posterior distribution, causing ratings for teams with limited games to shrink toward the league average, reducing overreaction to early volatility. As more data accumulates, decreases, allowing ratings to more closely reflect observed EPA . This shrinkage enhances predictive , with simulations propagating uncertainty into season-long projections such as playoff probabilities. As of 2025, projections use 20,000 simulations, while uses 10,000.

Data Inputs and Adjustments

The Football Power Index (FPI) relies on play-by-play data to generate expected points added (EPA) metrics, which form the core of team ratings for offense, defense, and special teams. For both and variants, current-season EPA is derived from ongoing game performances, while preseason and early-season baselines incorporate data from up to four prior seasons, with the most recent year weighted approximately twice as heavily to emphasize recent trends. Schedule strength is iteratively calculated through opponent adjustments, ensuring that a team's ratings reflect the of faced, using to update components after each game. Several situational adjustments are applied to refine EPA and game predictions, accounting for non-performance factors. Home-field advantage is a standard component, typically valued at around 2.5 points but varying by due to factors like altitude and effects; for instance, it is explicitly modeled in game site predictions. Travel distance introduces penalties, such as approximately 0.5 points for extreme cross-country trips in the (e.g., Seattle to ) or -1 point per 1,000 miles more traveled than the opponent in . Rest disparities are adjusted similarly, with an extra week of rest conferring about +1 point in the or +1 point per 5.5 additional days in , reflecting recovery advantages. Additional inputs incorporate environmental and personnel variables to enhance predictive accuracy. Weather effects are modeled for outdoor stadiums, particularly in the where late-season conditions can reduce offensive EPA from 1.8 to 1.0 due to colder temperatures and wind. Injury adjustments focus on projected player impacts, with a heavy emphasis on quarterbacks in the —using past efficiency metrics, aging curves, and replacement-level estimates to downgrade team ratings for absences. In , preseason baselines further include returning production percentages (e.g., quarterbacks valued at ~3.3 points), alongside recruiting rankings from sources like , , and Phil Steele, to account for roster continuity. FPI ratings are recalculated weekly following games, integrating new EPA data while gradually diminishing the influence of preseason priors as the season progresses; preseason values are established through simulations incorporating these inputs.

Variants and Adaptations

NFL FPI

The NFL variant of the Football Power Index (FPI) incorporates league-specific adjustments to account for professional dynamics, such as high player turnover and environmental factors, while maintaining the core simulation-based framework. Developed by ESPN's team, it emphasizes performance as a key differentiator, modeling each signal-caller's efficiency using components similar to Total QBR, adjusted for age, past performance, and replacement-level backups in cases of or . This QB factor significantly influences team ratings, as elite quarterbacks can elevate offensive efficiency by several points in projections. Unique to the NFL, FPI includes environmental and logistical adjustments, such as an altitude boost of approximately 0.3 points per home game for the due to the thin air at , which affects visiting teams' stamina and performance. Additional tweaks account for rest advantages (about 1 point per extra day of preparation) and extreme travel distances (up to 0.5 points in cases like cross-country matchups). These modifications reflect the NFL's structured schedule and venue variations, contrasting with the base model's broader applicability. Season-long trends are also factored in, with offensive EPA per game declining from around 1.8 early in the year to 1.0 later, allowing ratings to stabilize more rapidly over the 17-game slate compared to longer college seasons. Projections under FPI prioritize outcomes like division titles, wild-card berths, and probabilities, derived from 10,000 season simulations that integrate current form and opponent strength. For the 2025 season, the model adheres to the established 17-game format despite ongoing league discussions about potential expansion to 18 games, ensuring consistency in forecasting playoff probabilities—for instance, assigning teams like the a strong chance to win based on midseason data. Due to the 's annual roster flux, with teams typically retaining only about two-thirds of prior-year players, the system places greater weight on recent games (spanning 2-3 seasons of priors) for quicker adaptation to changes like free agency and drafts.

College Football FPI

The College Football Power Index (FPI) adapts the core methodology to account for the unique dynamics of the sport at the collegiate level, including annual roster turnover, recruiting influences, and varying schedules. Unlike variants, it incorporates college-specific factors such as returning player production to adjust preseason ratings, where teams retaining a high level of prior-year output from starters can receive boosts in their overall rating. Recruiting class rankings from sources like and serve as preseason priors, providing a foundational estimate of team talent before the season begins. Additionally, adjustments for coaching stability penalize teams with new head coaches by regressing their ratings slightly to reflect potential adaptation challenges. To address structural differences across the FBS landscape, the model clusters teams by conference strength in its simulations, which can highlight disparities such as the SEC's historical edge in projected win totals due to tougher intra-conference matchups. Non-conference scheduling also factors in, with penalties applied for weaker opponents that dilute a team's and lower their projected performance in 20,000-season simulations. Since the implementation of the 12-team format in 2024, FPI projections have expanded to include detailed odds for at-large berths, first-round byes, and semifinal appearances, simulating the new structure to forecast postseason paths more accurately. Projections under College FPI emphasize outcomes tailored to the college calendar, such as probabilities (typically requiring 6+ wins), conference championship chances, and (CFP) qualification odds, derived from repeated schedule simulations that incorporate game-specific variables like travel and rest. For the 2025 season, these projections integrate updated returning production data to reflect transfer portal activity, where incoming transfers are weighted at half value for key positions like , helping to model roster flux post-portal windows. This approach underscores the model's focus on forward-looking performance, with outputs like a team's chance of reaching 10 wins or securing a top-4 seed in the playoff. Key differences from the baseline FPI arise from college football's roster instability, employing longer historical priors spanning four prior seasons to stabilize preseason estimates amid high turnover rates averaging 40-50% annually. programs face inherently higher uncertainty in projections due to greater variance in recruiting and scheduling, leading to wider confidence intervals in outcomes compared to power-conference teams. These adaptations ensure the remains predictive for the irregular nature of college competition, prioritizing sustainability over short-term metrics.

Applications and Usage

In Predictions and Analysis

The Football Power Index (FPI) serves as a cornerstone for game predictions in both and , generating win probabilities and estimated point spreads derived from strength differentials adjusted for factors like home-field advantage and travel. For instance, an FPI differential of approximately 5 points between two teams typically translates to a projected of around 7 points in favor of the stronger , incorporating simulation-based outcomes from thousands of season scenarios. These projections inform ESPN's weekly picks, where FPI identifies potential upsets by highlighting games where implied win probabilities deviate significantly from historical norms, such as a favored with a 70% win chance facing an bolstered by rest advantages. In season-long analysis, FPI delivers projected win-loss records and power rankings by simulating each team's remaining schedule, enabling evaluations of overall performance trajectories. It also computes strength-of-schedule (SOS) rankings, which assess the difficulty of opponents based on their FPI ratings; for the 2025 NFL season, teams like the faced the toughest projected schedules early on, influencing mid-season adjustments to playoff odds. Similarly, in , FPI-driven SOS rankings highlighted challenging paths for contenders, such as those in the , contributing to preseason and in-season power rankings that guide conference title projections. Beyond core forecasting, FPI influences broader applications in football analysis, including advice where projected team efficiencies inform player value rankings and wire recommendations. Its outputs correlate closely with betting lines, as FPI win probabilities often align with Vegas , providing bettors with data-driven edges in markets like totals derived from expected point differentials. In media narratives, leverages FPI for in-depth articles on optimal matchups and championship contenders; for example, 2025 previews used FPI to spotlight high-stakes games and title for teams like and , shaping public discourse on playoff paths. FPI is frequently combined with advanced statistics for nuanced evaluations, such as assessing NFL depth charts by integrating offensive and defensive efficiency ratings with player-specific metrics like expected points added (EPA) to project lineup impacts. In trade analyses, ESPN analysts pair FPI projections with advanced stats to evaluate potential roster moves, for instance, simulating how acquiring a might shift a team's projected win total and playoff probability during the 2025 trade deadline discussions.

Integration with Other ESPN Tools

The Football Power Index (FPI) interconnects with 's broader analytics ecosystem to support team and player evaluations, drawing on its predictive simulations to inform complementary metrics. In , FPI complements SP+ rankings, which provide tempo- and opponent-adjusted efficiency measures, enabling analysts to offer a more nuanced view of team performance beyond FPI's overall power ratings. For the , FPI's team strength projections align with QBR assessments by contextualizing efficiency against projected opponent defenses, contributing to holistic evaluations of player impact within team dynamics. FPI powers a range of interactive tools and content platforms on , delivering visualizations of team rankings, win probabilities, and matchup projections to engage fans. For instance, it drives the Playoff Predictor, an interactive simulator where users input picks to generate outcomes based on FPI's 20,000-season simulations, highlighting playoff chances and title odds. These elements extend to broadcast and audio content, where FPI projections inform on-air graphics during games—such as overlays—and discussions on shows like "Get Up," providing real-time insights into game flow and season implications. Further expansions link FPI to ESPN's betting and fantasy offerings, enhancing user experiences through data-driven features. Via , FPI supplies a "betting edge" for early lines by comparing projected spreads from its simulations against market odds, aiding live wagering on NFL and college games. In fantasy platforms, FPI's team projections underpin draft rankings and player outlooks, factoring projected opponent strength into expected fantasy points for positions like quarterbacks and wide receivers. As of 2025, FPI has seen deeper integration with ESPN's -driven tools, merging simulations into post-game breakdowns via the generative avatar FACTS, which explains complex like FPI-derived efficiencies in highlights and recaps to educate viewers.

Evaluation and Criticisms

Accuracy and Performance

The Football Power Index (FPI) has demonstrated consistent predictive success in forecasting and outcomes, with accuracy rates varying by due to differences in structure and availability. For the , in its inaugural publicly available season (2015), the FPI favorite won 63% of games, aligning closely with the accuracy of Vegas closing lines and establishing it as a reliable predictive tool from the outset. This performance reflects FPI's use of efficiency metrics like expected points added (EPA) to simulate seasons and project results, providing a calibrated foundation for ongoing evaluations. In , FPI has shown higher historical accuracy, with the favored team winning 75% of FBS-versus-FBS games from 2005 to 2014 based on retroactive applications. Over the subsequent four seasons (up to 2016), this rate improved to 77%, outperforming preseason projections (72%) and demonstrating enhanced reliability as in-season data accumulates. When FPI projections diverged from Vegas lines during this period, the FPI favorite prevailed 55% of the time, underscoring its value in identifying edges beyond market consensus. Performance metrics further highlight FPI's robustness, including strong where teams assigned 70-80% win probabilities by FPI achieved actual win rates of 73%. Benchmarks against alternative systems, such as Sagarin ratings, indicate comparable , with FPI ranking among the top models for absolute error in recent seasons like 2021. Against-the-spread (ATS) coverage hovers around market-neutral levels, typically near 50%, as seen in games where FPI matched broader ATS outcomes. FPI also exhibits high with final standings, often aligning projected efficiencies with division and playoff finishes, though exact coefficients vary by year. Longitudinal trends reveal FPI's predictive strength increasing mid-season, particularly after Week 6, when accumulated play-by-play data refines efficiency estimates and boosts in-season accuracy to levels like % in FBS games. However, outlier seasons such as —marked by disruptions including canceled games and irregular schedules—presented challenges, with FPI's projections affected by incomplete data inputs and team opt-outs, leading to deviations from typical benchmarks. Updates in subsequent years, including priors for roster changes, have sustained or improved overall rates, positioning FPI as a leading tool for both and forecasts. As of the 2025 season, FPI continues to be used for projections, with ongoing discussions of its accuracy, though specific updated metrics for 2022-2025 remain consistent with historical trends of 63-% game prediction rates.

Criticisms and Limitations

Critics have pointed out that the Football Power Index (FPI) places heavy emphasis on Expected Points Added (EPA) metrics for assessing offensive, defensive, and special teams performance, potentially limiting its ability to capture a full picture of . This quantitative approach has been noted to underweight intangible factors such as effectiveness and game momentum, leading to predictions that prioritize statistical efficiency over broader contextual elements. In applications, FPI has shown a against , with calibration analyses revealing that lower-seeded teams, including entrants, consistently overperform relative to projected win probabilities across multiple seasons. For instance, actual outcomes exceeded FPI predictions in seven of ten probability bins, suggesting systematic undervaluation of non-favorite teams in high-stakes scenarios like . Similar issues have appeared in , where FPI rankings have drawn criticism for undervaluing teams like Ohio State; in the 2023-24 season, the Buckeyes were placed at No. 4 despite their evident talent, prompting accusations of misalignment with on-field potential. Biases in FPI projections have also been highlighted, particularly in where the model appears to favor () teams through schedule strength priors. Following Week 1 of the 2025 season, faced widespread accusations of SEC favoritism, with nine SEC programs occupying the top 15 spots despite losses by key teams like (ranked No. 13 after falling to Florida State, which went unranked). This pattern has fueled debates over whether preseason assumptions inflate SEC projections at the expense of other conferences. FPI's limitations include high variance in early-season projections and a general disregard for qualitative aspects like team chemistry, as the model leans on probabilistic simulations rather than holistic in-game narratives. Betting evaluations indicate inconsistent performance, with FPI yielding negative long-term returns (-2.5% as of ) compared to market odds and volatile year-to-year results, such as a +17.7% return in 2014 dropping to -24.5% in 2020. Additionally, the model's scores have trended upward over time (from 0.207 in 2014 to 0.235 in ), signaling declining as of that period. ESPN has responded to some critiques by iteratively updating FPI, incorporating adjustments for recruiting and roster changes in the 2025 version to better reflect modern dynamics. However, analysts like Bill Connelly have observed that FPI occasionally undervalues teams with strong personnel, such as (ranked No. 39 preseason despite top-tier defensive projections), suggesting it may lag behind more nuanced models in capturing forward-looking team strength.

References

  1. [1]
    Everything you need to know about College FPI - ESPN - Stats & Info
    Aug 10, 2016 · FPI is a predictive rating system designed to measure team strength and project performance going forward. The ultimate goal of FPI is not to ...
  2. [2]
    Guide to NFL FPI - ESPN - Stats & Info
    Aug 22, 2016 · NFL FPI is a prediction system for the NFL. Each team's FPI rating is composed of a predicted offensive, defensive and special teams efficiency.<|control11|><|separator|>
  3. [3]
    College Football Power Index 2025 - ESPN
    View the 2025 College Football power index on ESPN. The FPI is the best predictor of a team's performance going forward for the rest of the season.
  4. [4]
    NFL Football Power Index 2025 - ESPN
    The Football Power Index (FPI) is a measure of team strength that is meant to be the best predictor of a team's performance going forward for the rest of the ...
  5. [5]
    How ESPN's NFL Football Power Index was developed
    Aug 31, 2015 · The ultimate goal of FPI is to correctly predict games and season outcomes such as the chance to make the playoffs or win the Super Bowl. Some ...
  6. [6]
    College Football Power Index 2020 - FPI - ESPN
    Jul 21, 2021 · FPI represents how many points above or below average a team is. Projected results are based on 20,000 simulations of the rest of the season ...
  7. [7]
    Expected points and EPA explained - ESPN
    Sep 15, 2012 · Expected points (EP) accounts for factors such as down, distance to go, field position, home-field advantage and time remaining.
  8. [8]
    Understanding ESPN Team Rankings
    Oct 11, 2013 · Football Power Index. • The Football Power Index was developed by looking to "predict" games historically. Using many years of pregame ...
  9. [9]
    Will the 2020 NFL season see the death of home-field advantage?
    Sep 7, 2020 · Casual betting conversations routinely reference the home field as worth three points to the spread, but that has never truly been the case.Editor's Picks · Nfl & Cfb Best Bets · Road Hurdles Lowering
  10. [10]
    NFL Home Field Advantage Tracker | Historical Trends & Stadium ...
    For decades, the conventional wisdom in sports betting and analytics circles held that home field advantage was worth approximately 2.5 to 3 points in the NFL.Home Field Advantage Trends · Stats By Stadium · The Challenge Of Predicting...<|control11|><|separator|>
  11. [11]
    https://www.espn.com/nfl/story/_/id/46874499/2025-nfl-playoffs-chances-odds-tiers-32-teams-standings-nfc-afc
  12. [12]
    https://www.espn.com/nfl/story/_/id/46833755/espn-nfl-2025-midseason-report-afc-nfc-questions-stats-fantasy
  13. [13]
    Introducing ESPN's NFL Football Power Index
    Aug 31, 2015 · EPA per play is a measure of efficiency that serves as the basis for how FPI evaluates individual units and quarterbacks. Rest: Teams with a ...
  14. [14]
    NFL Football Power Index: 2025 projections, Super Bowl chances
    May 28, 2025 · ESPN's Football Power Index ratings are live. Let's predict the 2025 season, including playoff chances for 32 teams and potential draft ...Missing: College | Show results with:College
  15. [15]
    2025 College Football Power Index: Best matchups, title odds - ESPN
    Jun 3, 2025 · The FPI is a predictive rating system that estimates each FBS team's strength (in points per game relative to the national average) on offense, defense and ...
  16. [16]
    College football 2025 returning production for all 136 FBS teams
    Feb 24, 2025 · Looking through the prism of returning production data of every FBS team, we'll break down how the percentage of returning players is trending.
  17. [17]
    What is NFL strength of schedule? 2025 rankings by team - ESPN
    Aug 6, 2025 · The New York Giants have the strongest schedule for the 2025-26 NFL season. Check out how all 32 teams rank from strongest to weakest.
  18. [18]
    FPI, LSU and how probability in betting can work for you - ESPN
    Aug 10, 2023 · Ultimately, the models' conclusion was that LSU had a 7% chance to win the national championship, while the odds implied a 3-4% range. That's ...Missing: differential conversion
  19. [19]
    2025 NFL trade deadline: Which teams should add, subtract? - ESPN
    Oct 30, 2025 · Could the Lions add help? Will the Jets trade stars? Should the Giants make any moves? Let's look at which teams might swing deadline deals.
  20. [20]
    What Does SP+ and ESPN's FPI Project for Georgia Tech vs Pitt?
    Sep 29, 2022 · ESPN uses its FPI (Football Power Index) as an advanced analytical model to help look at matchups and predict outcomes. In ESPN's own words: " ...
  21. [21]
    2025 college football Allstate Playoff Predictor - ESPN
    Select your favorite contender, make your picks for how its season will play out and see what the Allstate Playoff Predictor foretells.Missing: details | Show results with:details
  22. [22]
    https://www.sportsvideo.org/2024/11/18/generative-ai-avatar-facts-announced-at-4th-annual-espn-edge-conference/
  23. [23]
    Generative AI Avatar 'FACTS' Announced at Fourth-Annual ESPN ...
    Nov 18, 2024 · FACTS will provide data and insights, from ESPN Analytics, in a compelling new way to help foster engagement and educate fans on complex sports analytics.
  24. [24]
    Enhancing ESPN's Game Recaps for Underserved Sports Using AI
    Sep 5, 2024 · ESPN plans to utilize AI technology to produce text game recap stories of select sports events, which will appear on ESPN digital platforms.
  25. [25]
    ESPN's Football Power Index: A look back
    Dec 18, 2014 · We have been able to retroactively apply FPI to the past 10 seasons. Since 2005, FPI has correctly predicted 75 percent of FBS games. It is ...
  26. [26]
    None
    ### Summary of Criticisms, Limitations, Biases, and Inaccuracies of ESPN FPI
  27. [27]
    ESPN's FPI shows SEC bias and disrespect to Ohio State football team
    Jul 28, 2025 · ESPN's FPI disrespects the Ohio State football team and the rest of the Big Ten. According to the FPI, Ohio State has just a 10.8 percent chance ...
  28. [28]
    ESPN Faces Accusations Of Major 'SEC Bias' Following Week 1
    Sep 1, 2025 · ESPN is facing accusations of "major SEC bias" heading into the second week of the 2025 college football season.
  29. [29]
    Coach Accuses ESPN of Rigging College Football Rankings
    Aug 19, 2025 · FPI, along with other metrics from ESPN, have been criticized for its inaccuracy, relying more on probabilities and less on in-game action.
  30. [30]
    2025 College Football Power Index: Who's undervalued ... - ESPN
    Jun 3, 2025 · ESPN has released its 2025 Football Power Index (FPI) ratings and projections, and our college football reporters are here to break them down.