Summary
Discover the secrets behind SEC's dominance in college football by delving into advanced metrics and historical trends. Key Points:
- Advanced metrics such as Adjusted Yards per Pass Attempt and Havoc Rate reveal why SEC teams consistently outperform others.
- Quantifying team performance with Success Rate and Line Yards offers deep insights beyond traditional statistics, showcasing each team's strengths and weaknesses.
- Analyzing Texas's recent rise through advanced metrics helps assess its sustainability compared to historical data.
Expansion Fueling SEC′s Dominance and Financial Growth
Both Texas and Oklahoma have strong traditions and loyal fan bases, which could give them an edge in competing for SEC titles. The addition of these two programs could also lead to increased revenue for the SEC, which could be used to invest in new facilities and initiatives.Key Points Summary
Insights & Summary
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Extended Comparison:| Platform | Features | Latest Trends | Expert Opinions |
|---|---|---|---|
| ESPN | Live scores, video highlights, exclusive college football games on ESPN+, College Pick'em game | Increased focus on streaming exclusive games via ESPN+ | Recognized for comprehensive coverage and timely updates |
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| CBS Sports | Latest news, live scores, player stats, standings, fantasy games, projections | Enhanced user experience with interactive features like fantasy games and projections tools | Praised for detailed player stats and advanced metrics insights |
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The goal of this analysis is to use historical advanced metrics to identify which teams in the SEC are true contenders, solid second-tier competitors, and mere pretenders. To achieve this, I delved into a comprehensive dataset of college football statistics spanning from 2001 to the present day, sourced from the Python library cfbd.
%%capture %pip install cfbd flatdict --quiet # install cfbd and the flatdict library in Google Colab import cfbd import pandas as pd from matplotlib import pyplot as plt import flatdict #setting some configurations pd.set_option('display.max_columns', None) # do not truncate columns plt.style.use('ggplot') CFBD_API_KEY = # just paste in your API Key here Within this data repository, you can access a variety of tables containing different datasets. The specific dataset I examined includes advanced statistics compiled at the season level. This analysis encompasses each team within the SEC, with the addition of Oklahoma and Texas for comprehensive coverage. Below is the function crafted to generate this dataset:
def generate_advanced_team_season_stats_df(team, year): #grab the advancated teams data games = api.get_advanced_team_season_stats(team=team, year=year) #initialize empty dictionary which we will pass in to our DataFrame. games_df_data = dict() for game in games: #convert the game object to dictionary since it's something else at the moment even though it looks like one. Won't be able to access dictionary methods. game_dict = game.to_dict() #flatten the dictionary, use the delimiter . flattened_game_dict = flatdict.FlatDict(game_dict, delimiter='.') for k, v in flattened_game_dict.items(): #if this is the first dict we've gone through, initialize our empty DataFrame with the correct kets if k not in games_df_data: games_df_data[k] = [] #otherwise append. We are using a list here but we could use Series or list objects to contruct DataFrames games_df_data[k].append(v) return pd.DataFrame(games_df_data)Quantifying Team Performance: Beyond Traditional Metrics
In evaluating team performance, it's crucial to understand both the offensive and defensive capabilities of a team. One way to measure this is through Offensive Predicted Points Added (offensive PPA) and Defensive Predicted Points Added (defensive PPA). Offensive PPA quantifies how many points an offense is expected to score based on specific game situations, such as down, distance, and field position. Conversely, defensive PPA assesses how many points a defense is likely to allow under similar conditions.Beyond these metrics, analyzing success rates on different types of downs provides deeper insights into a team's effectiveness. The Success Rate on Standard Downs measures the percentage of plays where an offense gains at least 40% of the necessary yards on standard downs—these typically include 1st and 10, 2nd and short-to-medium yardage (1-8 yards), and 3rd and short yardage (1-4 yards). On the other hand, Success Rate on Passing Downs focuses on those critical moments when the offense faces tougher situations like 3rd and long (5+ yards) or 4th down scenarios requiring significant yardage. This metric shows how often teams successfully navigate these high-pressure plays.
By combining these advanced statistics with traditional analysis methods, we can achieve a more comprehensive understanding of team dynamics in various game contexts. This multi-faceted approach allows us to appreciate not just overall performances but also situational efficiencies that can be pivotal in deciding game outcomes.
I conducted a comprehensive analysis for each metric that was either summed or averaged from 2001 to 2023. To illustrate, here's what the data reveals when we focus solely on the most recent decade:
After I managed to gather data spanning from 2001 to 2023, I analyzed how often each team featured in the SEC conference's top 5 for various metrics. Additionally, I tracked the frequency of teams making it into the top 3. The code used for these calculations is shown below:
frequency_counts = {} # Iterate over each category and its top 5 teams for category, teams in data.items(): for team in teams[:5]: # Only consider the top 5 teams if team in frequency_counts: frequency_counts[team] += 1 else: frequency_counts[team] = 1 # Print the frequency counts for team, count in frequency_counts.items(): print(f"{team}: {count}") # Alternatively, you can sort the results if desired sorted_counts = sorted(frequency_counts.items(), key=lambda item: item[1], reverse=True) print("\nFrequency of Each Team Appearing in Top 5:") for team, count in sorted_counts: print(f"{team}: {count}") frequency_counts = {} # Iterate over each category and its top 3 teams for category, teams in data.items(): for team in teams[:3]: # Only consider the top 3 teams if team in frequency_counts: frequency_counts[team] += 1 else: frequency_counts[team] = 1 # Print the frequency counts for team, count in frequency_counts.items(): print(f"{team}: {count}") # Alternatively, you can sort the results if desired sorted_counts = sorted(frequency_counts.items(), key=lambda item: item[1], reverse=True) print("\nFrequency of Each Team Appearing in Top 3:") for team, count in sorted_counts: print(f"{team}: {count}")Here is an example of the output illustrating the leading three outcomes within the period from 2001 to 2023:
While evaluating a program's track record over two decades provides valuable insights, it doesn't necessarily reflect its current standing in the competitive arena. To gain a more balanced view of a team's present status, I applied the same analytical approach to four additional time frames: the past decade, the past five years, the past three years, and the most recent year. These shorter periods emphasize recent performance trends without disregarding each school's historical context.}
{Assessing how a program has fared over a span of more than 20 years offers significant perspective; however, it may not accurately represent where that program stands today within its competitive environment. To counter this potential imbalance, I conducted similar analyses for four narrower time frames: the last ten years, five years, three years, and one year. By doing so, we place greater emphasis on recent performances while still acknowledging the historical narratives that shape each institution.
Upon completing the task, I consolidated the different datasets based on their respective time ranges into a single comprehensive list. This compilation was categorized by how often teams ranked in the top 5 and top 3 across all evaluated criteria. Below is the resultant data from this effort:
In the final step, I allocated a 'Rank Score' to each team based on their frequency during the specified timeframe, with scores ranging from 5 to 1. Teams appearing more frequently received higher scores. By summing these values, we arrived at the Program Rank Score displayed below:
Texas′ Recent Success: A Sustainable Surge or a Temporary Spike?
Oklahoma's consistent dominance across various time frames is a testament to their sustained success and program stability. They have managed to secure top 5 and top 3 appearances in nearly every category, except for the top three from the last three years. This highlights their long-term excellence and ability to remain competitive over different periods.In contrast, Texas has experienced a recent surge in the rankings, particularly with their performances last year that saw them achieve top 5 and top 3 placements. However, this raises questions about the sustainability of their performance. Historically, Texas has struggled to achieve back-to-back seasons with more than 10 wins. Additionally, head coach Steve Sarkisian's limited experience in leading consistently successful campaigns adds another layer of uncertainty regarding their ability to maintain this level of competitiveness moving forward.
Since the turn of the millennium, the Oklahoma Sooners have experienced only one season with fewer than seven wins. Remarkably, they haven't seen consecutive seasons with under ten victories since 1998-1999. Reviewing these statistics, it becomes evident that the betting lines may not reflect the team's true potential. The data suggests that the Oklahoma Sooners could indeed be an underestimated powerhouse waiting to awaken.
References
NCAA on ESPN - College Football Scores, Stats and Highlights
Visit ESPN for NCAA live scores, video highlights and latest news. Stream exclusive college football games on ESPN+ and play College Pick'em.
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Get the top news, scores, highlights and latest trending topics in FBS college football here at NCAA.com.
Source： NCAA.comCollege Football News, Scores, Stats, Standings, and Rumors
CBS Sports has the latest College Football news, live scores, player stats, standings, fantasy games, and projections.
Source： CBS SportsCollege football
College football is gridiron football that is played by teams of amateur student-athletes at universities and colleges. It was through collegiate ...
Source： WikipediaNCAAF College Football News, Video, Rumors, Scores, Stats, Standings
Select a League or Sport · 2025 EDGE Zion Grady commits to Ohio State over Auburn, others · 2025 3-star WR Tristan Norman has committed to Texas A&M.
Source： Yahoo SportsNCAA college football scores
Live college football scores, schedules and rankings from the FBS, searchable by conference.
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Be the best College Football fan you can be with Bleacher Report. Keep up with the latest storylines, expert analysis, highlights, scores and more.
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