This post describes our methodology and process for creating college football preseason rankings for all 130 teams in the Football Bowl Subdivision (FBS).

As one would expect from TeamRankings, our CFB preseason rankings are driven by stats and modeling, and not by less objective methods like film study or media scouting reports. However, we still apply a dose of subjectivity to fine tune these rankings.

Before we dive into the details of our approach, let’s cover a few basics.

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What Our College Football Preseason Rankings Represent

First, it’s important to know that at TeamRankings, our preseason rankings simply represent the rank order of preseason predictive ratings that we generate for every college football team.

So to create our preseason rankings, the first thing we do is calculate preseason ratings for every team.

Predictive Rating Definition

In simple terms, a team’s predictive rating is a number that represents the margin of victory we expect when that team plays a “perfectly average” FBS team on a neutral field.

This rating can be a positive or negative number; the higher the rating, the better the team. A rating of 0.0 indicates a perfectly average team.

Finally, because our predictive rating is measured in points, the difference in rating between any two teams indicates the projected winner and margin of victory in a neutral-site game between them.

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What Our College Football Preseason Rankings Represent

How Ratings Translate To Predictions

For example, our system would expect Alabama, which has a 2022 preseason rating of 33.4, to beat an average FBS team (with a 0.0 rating) by about 33 points on a neutral field.

It would expect Alabama to beat New Mexico State, which has a -26.9 rating, by about 60 points. And New Mexico State would be expected to lose to an average team by about 27 points.

Ratings Are More Precise Than Rankings

Understanding the nature of predictive ratings is critical, because they are a more precise metric than a simple ranking.

For example, Oklahoma fans may cringe to see Clemson ranked ahead of them, at No. 4 in our 2022 preseason rankings. However, Oklahoma’s predictive rating is 18.7, only 3.2 points lower than Clemson’s rating.

So yes, if you put a gun to our head and forced us to rank order every team, we’d say Clemson is going to be better than Oklahoma this season. But the difference isn’t that significant.

However, it’s a 7-point drop in preseason rating between No. 3 Georgia and No. 4 Clemson, which is more significant. That means we have a pretty clear top three teams in 2022.

So don’t place too much stock in a team’s ranking. Ratings tell the more refined story.

When and Why We Make College Football Preseason Ratings

Once the college football season starts, our predictive ratings go on autopilot. As game results from CFB Week 1 and beyond come in, our system automatically adjusts team ratings (and the resulting rankings) within a few hours of receiving a new box score.

Teams that win by more than our ratings had predicted see their ratings increase. Teams that suffer worse than expected losses see their ratings drop. Software code controls all of the adjustments and no manual intervention is required.

Generating preseason ratings, however, involves a more labor-intensive process that we go through before every new season starts. What we are trying to do, in basic terms, is to pre-calibrate our predictive ratings system. We want to give it a smarter starting point than simply having every team start out with a 0.0 rating.

Put another way, our preseason ratings are our first prediction of what we think every college football team’s predictive rating will be at the end of the upcoming season. And we need to make that prediction before any regular season games are actually played.

Despite being a substantial challenge from a data perspective, our approach to this process is still mostly data-driven and objective. However, there are some judgment calls incorporated, which we’ll explain below.

Why We Make Preseason Ratings

Before we get into the details, it helps to explain a brief history of how and why our current preseason ratings process evolved:

• In the way old days (early 2000s), every team would start the season with a 0.0 rating, and we’d put a note on the site not to trust our ratings until Week 5 or Week 6. Before then, with such a tiny sample size of games, big surprises or lopsided results could really produce some really funky ratings.
• In the semi old days (mid to late 2000s), we started having each team begin the season with its end of season rating from the prior year. Until Week 5, the impact of the prior year rating would gradually decay to zero, and by Week 6, we’d only consider current season results. Better, but still not the best.
• Starting in 2011, we implemented the framework we use today. We looked at years of historical data and built a customized model to generate preseason ratings for college football. This approach is completely divorced from our automated in-season ratings updates.

Why we took that final step is simple. Generating preseason team ratings using a customized model significantly improved the in-season game predictions made by our ratings — and not only in early season games, where one would logically expect to see the biggest improvement.

In fact, still giving the preseason ratings some weight even at the very end of the season improved our prediction performance over the final weeks too.

The payoff was clear and measurable. In 2018, our predictive ratings ranked fifth in the NCAA Football Prediction Tracker (out of 60+ systems tracked) for predicting game winners. In 2017, we ranked sixth. Outside of the updated Vegas line, TeamRankings was the only ratings system to crack the top six in both years. We are also the only rating system to finish in the Top 10 every year from 2017 to 2020.

When We Make Preseason Ratings

During every college football offseason, we first put in work to improve our preseason ratings methodology. We investigate new potential data sources, and refit our preseason ratings model using an additional year of data.

After implementing any offseason refinements to our process and model, we then gather the necessary data from various sources, and generate our preseason ratings for the upcoming season. We typically complete the process a week or two before the regular season starts.

How We Make College Football Preseason Ratings

Now let’s get to the meat. By analyzing years of historical college football data, we’ve identified a short list of descriptive factors that have correlated strongly with end-of-season power ratings.

Some of these predictive factors include:

• Prior season performance. How good a team was in the latest season, measured by predictive rating, not win-loss record.
• Program success history. How good a team has been in recent history, not including the latest season. A basic measure of legacy of success, the ability to recruit and develop new talent, etc.
• Returning strength. The percentage contribution to key stat categories we’ve identified, such as passing and rushing effectiveness, that is returning for the upcoming season.
• Prior season luck. Last year’s performance in stat categories highly impacted by luck, or not very reproducible for other reasons (e.g. turnover margin, red zone defense).

We use a regression model to determine each factor’s weight in our preseason ratings. As a result, the relative importance of each stat factor is based on its demonstrated level of predictive power in past seasons.

Example Predictive Factor: Defensive Lineman Continuity

To illustrate how we identify and incorporate specific predictive factors, let’s look at an actual stat used by our model: the percentage of defensive lineman games played returning. Let’s call it RetDL% for short.

At a very basic level, RetDL% measures the continuity of a team’s defensive line. High-usage defensive linemen from the previous season can graduate, get drafted into the NFL, or otherwise leave the team, so we wanted to see what happened based on how much of that experience needed to be replaced.

So first, we counted up all the games played in the prior season by all players whose main position was on the defensive line. That’s the denominator.

Then we did the same thing, but only counted players who are on this season’s roster. That’s the numerator.

The second number divided by the first number equals the RetDL%. And as it turns out, entering the 2019 season, in our historical training data set there were:

• About 25 teams with RetDL% higher than 90%. Their “next season” ratings increased by an average of +2.5 points.
• About 200 teams with RetDL% between 75% and 90%. Their “next season” ratings increased by an average of +1.8 points
• About 600 teams with RetDL% between 55% and 75%. Their “next season” ratings, on average, stayed the same
• About 200 teams with RetDL% between 40% and 55%. Their “next season” ratings decreased by an average of -1.1 points
• About 25 teams with RetDL% lower than 40%. Their “next season” ratings decreased by an average of -4.6 points

Across a sample size of more than 1,000 team-seasons, that’s some fairly convincing evidence that defensive line continuity is a plus when it comes to predicting the upcoming season.

If all you knew was the percentage of defensive lineman games played returning for every team, you could already begin to make an educated guess as to whether the team will be better or worse this year.

A regression model does this level of analysis for all of the metrics we examine, only it breaks the data down much more granularly.

Step 2: Review & Refine The Initial Results

After our model generates its 100% data-driven preseason ratings for college football, we then compare those ratings (and the resulting team rankings) to the betting markets and human polls.

If our assessment of a specific team seems way out of whack in comparison to those benchmarks, we’ll investigate more. Primarily, we’re looking to identify some factor not taken into account by our model (e.g. a coaching change or an abnormally good or bad recruiting class) that is likely to impact the expected performance level of a team.

In most of those cases, we end up adjusting our rating to be closer to the consensus. As a result, this final part of the process does inject some subjective judgment calls into our process.

Why Adjust CFB Ratings Manually?

We’re data guys, so it typically takes a lot of convincing for us to incorporate some level of subjectivity into our predictions.

In the case of college football preseason ratings, though, we think it makes sense. Especially with so few games played each season, there’s not much historical data to begin with in college football.

Yet there’s still a very high statistical bar to reach in order to anoint a particular stat as generally predictive of future performance. Consequently, very few stats pass the test.

That’s a good thing. One of the biggest challenges of predictive modeling is filtering out the signal from the noise, and “false positives” based on small sample sizes can ruin the future accuracy of a model.

At the same time, lots of different factors are still likely to impact the future performance of a particular team in some significant way. But until we have a large enough sample size of similar events to analyze, it would be very risky to incorporate them into our model.

The “Intangibles” Of College Football Preseason Ratings

In some cases, factors our model doesn’t currently consider are simply in areas where we haven’t yet built up an extensive historical database of relevant information. For example, in the future we plan to do more analysis of coaching changes and recruiting impact, after acquiring deeper data.

In other cases, the factors are a more unique or complicated — true outliers. Consider the hiring of a new offensive coordinator who, based on his 10-year track record, appears to be more skilled than the previous coordinator at implementing a scheme that best fits a team’s personnel, who are undersized but athletic.

There are so many contextual variables at play in a situation like that, it’s hard to even say if any other obviously-similar events have happened in recent college football history.

In those cases, manual adjustments to our initial data-driven ratings, in order to incorporate either crowd wisdom or betting market pricing, may continue to be our best solution for the foreseeable future.

Side Note: We Still Take Stands…

As a final point, it’s important to remember that predicting how good a team will be before the season starts (especially for a sport like college football with 130 teams to project) is one area where the betting markets and “expert” crowd wisdom have proven to be good predictors overall — but certainly not perfect.

And while it has its blind spots, our methodology is rooted in a level of statistical analysis that is far more rigorous than what most other rankings-makers apply. As a result, only in some cases will we adjust our numbers all the way to match the market consensus.

For example, in 2018 our single biggest preseason outlier vs. the AP Poll was Iowa. Our preseason rankings had Iowa at No. 26, while the AP Poll had Iowa at No. 41. The Hawkeyes ended up going 9-4, finished 15th in our predictive rankings, and 25th in the final AP Poll.

Conclusion

There are many different ways to make preseason rankings for college football. The approaches can vary greatly, from media polls to “expert” analysis, from building a complex statistical model to making inferences from futures odds in the betting markets.

And speaking frankly, there’s plenty of crap out there. But there’s also no Holy Grail (yet).

The primary goal of our preseason analysis is to provide a baseline (or “prior” in statistical terms) that makes our ratings better at predicting regular season games. For that purpose, we’ve settled on a mostly data-driven, but still subjectively adjusted approach for preseason ratings. For our goals, this approach has proven valuable.

Who’s College Football Preseason Rankings Are The Best?

Are our preseason rankings “the best” out there, compared to other methods? Honestly, we’re not sure. We haven’t yet done extensive historical comparisons to sources like the preseason AP Poll, ESPN’s preseason FPI rankings, or Bill Connelly’s preseason SP+ rankings. We hope to do more comparative research in the future.

However, asking which rankings are “best” is also a loaded question. Why? Because the goal of AP voters in ranking teams isn’t the same as our goal.

For example, we’d bet a lot of money that most AP voters care about a team’s win-loss record when they form their opinions on which teams should be ranked highest. Our ratings don’t give a hoot about win-loss record. All we care about is predicting margins of victory in future games.

Within ten seconds of looking over our preseason rankings, you’ll probably find several rankings you disagree with (vehemently), or that differ from what most other “experts” or systems think. That’s to be expected.

And when the end of this upcoming season arrives, we’ll have gotten plenty of teams wrong. (Not to mention that in some seasons, our preseason ratings will end up being a lot more accurate overall than in others.)

On balance, though, the system we’ve built has proven its value (to us, at least) over the long term.

Remember, if you’re in a football pool or planning on betting some games this football season, check out our Football Pick’em Pool Picks , NFL Survivor Picks and College Football Betting Picks.

The post How TeamRankings Makes College Football Preseason Rankings appeared first on Notes from the Sports Nerds.

UPDATE (Monday, September 26 at 1:30 PM ET): The storm has mostly passed Halifax. There were no major data disruptions.

As of Friday, September 23 at 3:00 PM ET, Hurricane Fiona is forecast to make landfall in the North Atlantic region Friday evening. The storm will likely miss most U.S. sporting events. However our main data provider, Gracenote, is located in Halifax, Canada, which is directly in the path of the hurricane. They alerted us this afternoon that there is a high chance of delayed data updates this weekend (September 23-25), as the safety of their staff take top priority.

We expect these will be the main impacts to TeamRankings, BetIQ, and PoolGenius:

• Power ratings calculated on September 24-26 may not include all games from the previous day.
• Team and player stats calculated September 24-26 may not include all games from the previous day. In addition, they may incorrectly assume that a team or player had zero of a given stat, when in fact they just have missing data.
• MLB game predictions on September 24-26 may be impacted, as the stats and power ratings they use as inputs may be affected.
• Game results across the site may not be updated in a timely manner. For example, team game logs may not show the scores of games, betting pick pages may show games as “Started” but not graded, and prediction accuracy pages may not include all games.

We do not expect major disruptions to our Survivor and Pick’em tools, nor to our NFL or college football game predictions. These rely on stats and ratings that have already been calculated (and so won’t be impacted by any data delays) and on betting odds data that is collected via automated scripts and does not rely on human data entry by our data provider. The main thing you may notice is picks not being graded in a timely manner. But pick advice and predictions should not be affected.

The post Possible Data Delays Due To Hurricane Fiona appeared first on Notes from the Sports Nerds.

If you haven’t visited TeamRankings.com in a while, you may notice a few changes we made recently. First, the site has an updated logo—not much different, but a bit spiffier than before.

Second, there’s a bar at the top of the site with links to “BetIQ” and “PoolGenius.” Here’s how it looks on mobile phones, for example:

What Are BetIQ and PoolGenius?

In short, we are transitioning from operating a single brand (TeamRankings) to operating a network of multiple brands (and eventually, different websites and mobile apps). Although we have a lot more work to do to execute that vision, we recently took the first steps.

For our site visitors and subscribers, this post lays out the immediate-term implications.

We Had One Brand. Now We Have Three.

For many years, TeamRankings has provided data and tools to sports fans, bettors, and pool players. We are now targeting those latter two audiences—bettors and pool players—with distinct brands.

As a result, we now own and operate three distinct brands:

• TeamRankings for statistically-minded league and team fans.
• BetIQ for sports bettors looking to make the most informed bets.
• PoolGenius for pool players serious about maximizing their edge.

We may introduce more brands in the years ahead. For the foreseeable future, though (let’s say the next 12-18 months), we plan to focus on building out and improving these three brands.

How The Move To Three Brands Affects Visitors & Subscribers

Our goal is to keep this initial transition from one brand to three brands as easy as possible for our existing subscribers and longtime site visitors. At the same time, it does necessitate some changes.

Here are the most important things to know:

• The three brands are not standalone web sites (yet).
  Technically, BetIQ and PoolGenius are still currently part of the TeamRankings.com web site, we’ve just made the pages included under each different brand look different. However, we do own the dot-com domains for all three brands (www.teamrankings.com, www.betiq.com, and www.poolgenius.com). Typing any of those URLs directly into your browser will send you to the homepage of each brand. FYI, we still haven’t built a mobile app for any brand, but we’d like to someday.
• User accounts are shared across all three brands.
  If you already have a TeamRankings user account, you do NOT need to create another account on BetIQ or PoolGenius. Whether you’re on a TeamRankings page, or a BetIQ page, or a PoolGenius page, you can click the “Log In” link at the top right of the screen and log in using the email/username and password associated with your existing TeamRankings account. Similarly, if you register for a new user account on TeamRankings, BetIQ, or PoolGenius, your account credentials will be shared across all three brands.
• Premium subscriptions can now include features across multiple brands.
  As with user accounts, you don’t need a separate premium subscription for TeamRankings, BetIQ, and PoolGenius. Our premium subscription packages remain the same as before, but they can now cover multiple brands. For example, our Yearly Subscription (which includes both betting picks and pool picks) now gives you access to all premium betting-related features on both TeamRankings and BetIQ, as well as access to all pool picks on PoolGenius.
• There is a navigation bar at the top of every page to switch between brands.
  Per the screenshot at the beginning of this blog post, one click is all it takes to switch between our three brands, from any page. You’ll always see that multi-brand navigation bar, whether you are on a computer, tablet, or mobile phone.
• There will be a transition period in the months ahead.
  We still have work to do to deliver the best user experience in our new three-brand model. In addition, we will undoubtedly continue to discover downstream impacts of this transition that we did not anticipate. Still, we felt ready enough to roll out these first steps of our longer-term vision, and we wanted to get the basics in place before the 2022 football season started. So please bear with us, and if you see anything that confuses you, let us know (support@teamrankings.com).

Features And Future Plans For Each Brand

Here’s a quick run-down of what currently lives under each brand, and our future plans:

TeamRankings

• TeamRankings will remain a general stats site and resource for sports data.
• Our four pool picks products (NFL Survivor Picks, Football Pick’em Picks, Bowl Pick’em Picks, and NCAA Bracket Picks) are no longer under the TeamRankings brand. They have moved to PoolGenius.
• In the short term, any sports betting-related data and pages on TeamRankings, such as betting picks and betting trends pages, will remain as-is.
• At some point in the future (timing still TBD), any data on TeamRankings that specifically relates to sports betting will be moved to BetIQ.
• Going forward, we plan to refocus TeamRankings on its original mission, which was to provide sports fans with smart data on their favorite leagues and teams.

BetIQ

• BetIQ is our new brand dedicated to sports bettors. It will offer both free and paid subscription-based features.
• It is currently in an early stage of development, and will continue to change substantially in the months ahead as we add new data and features.
• We will not start the process of removing sports betting-related data from TeamRankings and moving it over to BetIQ until we feel that BetIQ has reached an appropriate stage of development.
• Until we reach that point, some overlapping content will exist on both TeamRankings and BetIQ (e.g. betting picks and season projections).
• The predictive models for game picks (e.g. Decision Tree, Similar Games) and season projections currently shown on BetIQ and TeamRankings are the same. However, some predictive data is presented in slightly different ways across the two brands. Until all betting-related data exists only on BetIQ, you can use whatever predictions pages you like best, whether on TeamRankings or BetIQ.
• BetIQ is where we publish our new Staff Picks feature, which presents a short list of our favorite betting picks that incorporate both hands-on team and player research we conduct, along with insights from our algorithmic models.
• Premium subscription packages that include betting picks (e.g. our Yearly, Monthly, and Weekly Subscriptions, plus our Football Season Pass) now include access to all premium features on both TeamRankings and BetIQ.
• Some currently free betting-related data on TeamRankings may eventually require a paid subscription to access once we move it to BetIQ. But it’s still free for now.
• Going forward, we intend to expand the breadth and scope of features on BetIQ, covering more types of bets and more sports, and developing new bet discovery and research tools.

PoolGenius

• PoolGenius is our new brand dedicated to sports pool and office pool players. It will offer both free and paid subscription-based features.
• As of summer 2022, PoolGenius now houses our four current pool picks products: NFL Survivor Picks, Football Pick’em Picks, Bowl Pick’em Picks, and NCAA Bracket Picks.
• We are in the process of developing more original content about pools and pool strategy for PoolGenius, including a podcast and videos (coming soon).
• Going forward, we intend to expand the types of pools we cover on PoolGenius, and develop ancillary features useful to pool players (e.g. lists of pools you can enter).

Thanks And Stay Tuned…

As always, we appreciate the support of our subscribers and longtime visitors through this transition from one to three brands. We understand that change is almost never easier than keeping things the same.

But after much thought, we are confident that operating a network of brands is our best long-term strategy. If you’re curious why, we invite you to read our other announcement post that explains why we’re doing this.

Finally, if you’ve got any feedback or suggestions for us regarding this transition, we’d love to hear it. Just shoot us an email at support@teamrankings.com.

The post TeamRankings, BetIQ, and PoolGenius: A Guide To Our Three Different Brands appeared first on Notes from the Sports Nerds.

For a company that tends to prioritize product building over snazzy marketing, we recently took a pretty significant step in the branding department.

We have decided to move from operating a single brand (TeamRankings) to operating a network of multiple brands (and in the future, a portfolio of different websites and mobile apps).

We’ve published another blog post that explains the implications of this transition for our visitors and subscribers. But for the curious, we also wanted to explain why we’re making this change.

In short, we’re finally addressing a branding challenge that has plagued us for years.

We Had One Brand. Now We Have Three.

First, a quick recap of what we’ve done so far.

For over 20 years, TeamRankings has provided data and tools to sports fans, bettors, and pool players. We are now targeting those latter two audiences—bettors and pool players—with distinct brands.

As a result, we now own and operate three distinct brands:

• TeamRankings for statistically-minded league and team fans.
• BetIQ for sports bettors looking to make the most informed bets.
• PoolGenius for pool players serious about maximizing their edge.

We may introduce more brands in the years ahead. For the foreseeable future, though (let’s say the next 12-18 months), we plan to focus on building out and improving these three brands.

The Backstory

Here’s the context behind the branding and business challenges that led to this transition.

When original TeamRankings founder Mike Greenfield launched the first version of TeamRankings.com at the turn of the (21st) century, his primary motivation came from being a passionate team fan.

Mike was in his senior year at Stanford University, and he thought that based on an objective view of the stats, both the AP Poll and Coaches Poll were criminally underrating the Cardinal basketball team.

So Mike set out to prove the sports “experts” wrong by developing his own data-driven rankings of teams. He also wanted to learn about coding and web technology. Figuring out how to publish his rankings on the Internet—and to get them to update daily, in a fully automated way—seemed like a great learning project.

Hence TeamRankings.com was born. (Kind of wild that in 1999, that domain name was available for $9.99.)

Here’s a snapshot of the site from August 2000 from the always amazing Wayback Machine, back when the St. Louis Rams were coming off their Super Bowl winning season…

From Passion Project To Business

In his free time over the next few years, Mike continued to build out the site. He added more sports and started tinkering with predictive modeling, a budding interest of his. As time passed, he noticed a few things:

• Traffic was growing.
  As Mike continued to add unique and sophisticated data (by early 2000’s standards, at least), traffic to the site was growing steadily, despite him not spending a penny on marketing or advertising.
• Predictions were popular.
  Some of the site’s most popular pages were the ones that showed Mike’s computer-calculated daily game predictions for different sports. Those predictions were derived from Mike’s homemade predictive ratings as well as from another more sophisticated model he had built.
• Certain users were highly engaged.
  Internet technology was in its early days back then, and Mike’s self-taught coding skills weren’t the most advanced. As a result, technical glitches that crashed the site or stopped it from updating happened fairly frequently. Before long, support emails from anxious visitors began to pile up after a glitch. These emails shared a theme: “Are the predictions are going to be back up before today’s games start?!”

These observations kicked off the transformation of “TeamRankings, the hobby site” into “TeamRankings, the business.” Sports bettors clearly seemed to be getting some value from the site. And if people were staking a bunch of money on sports—via betting, or sports pools, or fantasy leagues, etc.—then at least some of them should be willing to pay for relevant data and useful analysis tools, right? Just like how serious stock traders all pay a bunch of money for Bloomberg terminals.

Thankfully, that hypothesis proved true—enough for us to build a successful small business around it, at least. (Who wants Bloomberg’s billions anyway!)

Getting the company off the ground was tough, and we were cautious with our approach to expansion in the early days. (In retrospect, too cautious.) But our business has accelerated in recent years, and today we’re investing more than we ever have into improving our products, growing our team, and providing more value to subscribers and visitors.

The Branding Problem

Based on how our business evolved, though, we knew we faced a branding challenge. Today there are around 200,000 pages of free sports data on TeamRankings.com. Lots of sports fans use the site on a daily basis, either to see where their favorite teams stand or as a general stats resource.

However, general sports fans have not been our primary focus in recent years. That’s because we currently make most of our money by selling premium subscriptions to sports bettors and pool players.

If you visit the TeamRankings homepage today (August 2022), for instance, it’s clear that our first priority is to make you aware that we sell betting and pool picks:

We don’t even have actual rankings of teams anywhere on the homepage, which seems a wee bit odd for a site literally called “TeamRankings.” And on the flip side, the name “TeamRankings” doesn’t exactly scream “this site offers premium services to sports bettors and pool players.”

Thinking To The Future

We’ve debated how to solve this branding problem for a while. On the one hand, TeamRankings is now a 20+ year old name familiar to many people. By virtue of it being around forever and showing up in a lot of Google search results, TeamRankings has already accumulated brand awareness among gamblers and pool players.

(That awareness would be even better if we had had any sort of a social media game going over the past decade, but we’ve been absolutely pathetic in that department. We’re working on it.)

In short, plenty of people already know that TeamRankings.com sells premium services for sports betting and pools. So messing with the status quo is a risk, and forces change on our most valuable audiences: existing subscribers and visitors.

On the other hand, we’re still a small business. The number of people who have never heard of us and whom we want to reach is far, far greater than the number of people who already know about us. So we want to optimize our marketing strategies for the future.

Having to get new users and prospective subscribers to understand that “TeamRankings” offers much more than just rankings would always be an impediment to growth. For example, a person looking for advice on an NFL survivor pool may see the URL “TeamRankings.com” and not even bother visiting the site in the first place.

From a branding perspective we were effectively getting the worst of both worlds:

• Undercutting a strong, targeted brand.
  TeamRankings is a fantastic name (maybe the best name) for a sports rankings site. But because of how our business evolved, we’re not exploiting that brand value to anywhere near the fullest extent. After being around for two decades, TeamRankings.com should be the site that millions of sports fans think of first when they want to see smart rankings and predictions for their favorite sport. Right now it is not.
• Forcing prospective customers to figure out what we do.
  If our existing brand was a sufficiently vague term (like “Apple” or “Yahoo!”) with no associated meaning in our industry, it would be easier for us to target different audience segments with a single brand. But that’s not the case. If a friend told you to visit a sports site called TeamRankings.com, you would have clear expectations of what you would find there. Our current homepage doesn’t meet those expectations. Instead, first-time visitors are forced to spend extra time understanding what TeamRankings does, assuming they have the patience to do so in the first place—and some percentage of them won’t.

Distinct User Populations

In terms of convincing us that it was time to make a change, the icing on the cake was some research into user behavior that we conducted, by analyzing our site’s traffic logs and doing some on-site testing.

In summary, we found that crossover usage of site pages that specifically targeted one of our core use cases (i.e. fan info, betting, or pools) was lower than we assumed it would be. In particular:

• Subscribers who had access to both betting-related features and pool-related features were typically much bigger consumers of just one of those two categories of features.
• When we aggressively promoted our premium subscriptions to new visitors who came to the site to look at content more indicative of being a general sports fan, those visitors converted at an extremely low rate.

Nothing is ever completely black and white when it comes to segmenting prospective customers. For example, there are surely sports bettors and fantasy/DFS players who are using the free data on TeamRankings.com for research, but who may never have any interest in subscribing to our premium services.

However, our general conclusion was that a majority of our users appeared to have a strong primary motivation for using TeamRankings.com—either for betting, or for pools, or for some other reason.

A Dose Of Contrarianism?

It’s also worth noting that the state-by-state legalization of sports betting in the U.S., kicked off by a 2018 Supreme Court ruling (thanks, SCOTUS!), has led to what can only be described as an infatuation with sports betting within the sports media and data industries.

For a profit-driven sports media business, the economic returns now possible by incessantly jamming sports betting related content down the throat of an audience may well end up being justified (in the short term, at least). However, we remain convinced that a substantial portion of U.S. sports fans do not and will never prefer to identify as “sports bettors” first, even if they place a wager here and there.

We’ve learned a lot over two decades of serving both fans and bettors, and this much is clear to us. There’s a way to speak to people who are primarily fans that occasionally bet, and a much different way to speak to people who wake up every morning excited to figure out what they’re going to bet on that day. (“Degenerates” is such a crass word, we prefer “Speculation Seekers” or “EV Enthusiasts.”)

We have a lot to offer both populations, and we now have distinct brands to optimize the messaging to each one: TeamRankings for the “fans first” group, and BetIQ for more active bettors.

One final thought here. Given the industry’s current obsession with sports betting, we think that there may be a corresponding decline of investment over the next few years in providing great information and experiences to everyday fans who simply don’t care all that much about betting. We consider that a longer-term opportunity, and as veritable dinosaurs of the Internet, we like playing the long game.

The Plan From Here

Given everything discussed above, splitting TeamRankings into a network of brands made the most sense to us. Now that our first three brands are all out in the wild, here’s our general plan for the near future:

• Increase subscriber value.
  Building useful products for sports bettors (via BetIQ) and pool players (via PoolGenius) will continue to be the focus of our premium subscription business. We’re increasing our investments of time and resources into these products. (On that note, we plan to drop some job postings in the near future, but if you’re looking for a new gig and think you can help us—especially if you’re an engineer or have data science skills—feel free to shoot us an email at jobs@teamrankings.com and show us what you can do.)
• Grow our audience.
  At the same time, we plan to renew our focus on the “general sports fan” use case via TeamRankings. Our goal is for TeamRankings to offer a much more brand-aligned set of free features that league and team fans love, which eventually will include more sports (we hear you, NHL fans!) and personalization of the site around your favorite sports, teams, and players. If we execute our plan well, a day will come in the not too distant future when TeamRankings.com delivers all that you expect (and more) from a site of that name.

If you got this far, thanks for reading. If you have any feedback or just want to give us your two cents about this business strategy decision, feel free to drop us an email at support@teamrankings.com.

The post Introducing BetIQ and PoolGenius, Two New Brands From TeamRankings appeared first on Notes from the Sports Nerds.

After an unusual 2020 MLB season that didn’t start until July and lasted for only 60 games, we are ready for the return of a more normal schedule in the 2021 season. That season kicks off in a few days, on Thursday, April 1st, with all 30 teams in action. Today, we unveil our 2021 projected MLB standings and power ratings.

As always, the main purpose of our preseason MLB ratings is to provide a data-driven starting point for our MLB projected standings.

They also drive our MLB postseason seed projections and our other MLB season projection details. These include fully automated win-loss predictions, playoff chances, and World Series win odds.

We’ll update those projections every day to reflect the latest results and our most up to date MLB power ratings.

How We Create The Preseason Ratings

For football and basketball, we use our own data and models to come up with independent estimates of team quality. We then compare those to the market, and to other projections, and make final adjustments.

We treat baseball a bit differently, though. So far at least, our methods for projecting MLB aren’t as cutting edge, relative to other sports.

So rather than trying to create our own preseason ratings, and deriving a season projection from those, we base our initial MLB projected standings on a weighted average of betting market info and projected standings from other well respected sources.

Essentially, we combine projected win total info from various sources into a consensus win total projection for every team. Then we figure out what preseason team ratings would lead to those exact projections.

We’re still publishing these, so that you know what the initial rating in our projection system was for each team. But at this point we can’t recommend using these MLB projected standings to go place preseason bets, for example, if for no other reason than we haven’t done extensive backtesting of our approach.

A Seemingly Narrow Win Distribution

You may look at the projections below and think that they aren’t extreme enough. Only three teams are projected with at least 92 wins, for example, and only one team is projected for more than 100 losses. In a way, you’d be right —when the dust settles on the season, there will likely be several division winners, and maybe a few wild card teams, that have won more than 92 games.

But for most teams, besides the heaviest favorites, if a team wins that many games, it’s going to be because things have gone better than could have reasonably been expected to start the season. And picking which teams will wildly exceed expectations is rather tricky. On average these conservative predictions should provide a less biased starting point than more aggressive ones.

More Team Projection Details

If you’d like to see our more aggressive best case and worst case scenarios for each team, check out their team projections page. Here are the LA Dodgers projections and the Colorado Rockies projections as examples.

Click through to find a chart showing the projected odds of the Dodgers or Rockies winning any specific number of games. From the details in the Rockies’ chart, you can determine that we project about a 4.6% chance for them to win 50 or fewer games in 2021.

The projections detail page also includes a list of each team’s toughest & easiest games, and a table showing how their chances of winning the World Series change depending on what seed they get in the playoffs.

2021 MLB Projected Standings Highlights

It was not too long ago that the American League was the dominant league, with most of the top teams. This year, though, we project the defending World Series champion Dodgers to be the best team, and for four of the top five teams to be from the National League. The New York Yankees are the only American League team in our preseason top five.

Some other highlights:

• The San Diego Padres are a clear contender for the World Series, and rate as the third-best team. Because they are in the same division as the Los Angeles Dodgers, though, they have a lower percentage chance of winning their division (and thus avoiding the Wild Card Round) than seven other teams that we rate below San Diego.
• No team in the NL Central is rated above No. 15 in our power ratings, and four teams in the NL East are rated higher than anyone from the NL Central.
• The defending AL Champs, Tampa Bay, are not currently projected among the five most likely playoff teams in the American League, though they are only slightly behind the two projected wild card teams, Toronto and the Chicago White Sox.
• The Los Angeles Angels and Oakland Athletics are rated the same, but the Athletics have a slightly higher win projection, and a 3% greater chance of reaching the playoffs. Why? Because the Angels get an extra 3-game series with the Dodgers, while the Athletics get two more games against the Giants and an extra game against the Diamondbacks compared to the Angels. We will see if those handful of differences in the schedule matter in a playoff chase.

2021 MLB Projected Playoff Results

Here is how the playoffs would play out, if these projections are spot on (numbers below refer to the team’s playoff seed within their league).

Wild Card Round:

• #5 Toronto Blue Jays over #4 Chicago White Sox
• #4 San Diego Padres over #5 Atlanta Braves

Division Round:

• #1 New York Yankees over #5 Toronto Blue Jays
• #3 Houston Astros over #2 Minnesota Twins
• #1 Los Angeles Dodgers over #4 San Diego Padres
• #2 New York Mets over #3 Milwaukee Brewers

League Championship Series:

• #1 New York Yankees over #3  Houston Astros
• #1 Los Angeles Dodgers over #2 New York Mets

World Series:

• #1 Los Angeles Dodgers over #1 New York Yankees

Full Preseason 2021 MLB Projected Standings

The post 2021 MLB Projected Standings & Preseason Ratings appeared first on Notes from the Sports Nerds.