BYU’s Experience a Predictor of a Big Season?
Louis E. Deaux is a contributor to Deep Shades of Blue. Deaux is a member of the Football Writers Association and has covered college athletics for decades, including writing for the Football News for many years, covering the WAC and MWC. –
Statistics and mathematical patterns have always intrigued me.
As a professional that works with numbers and geometry in multiple dimensions, my mind tends to wrap itself around problems that require the methodology of analysis most often associated with changing forces.
Calculus is of course the branch of mathematics that deals with changes in forces and the outcome of forces as conditions change. For this reason, I am always intrigued by the way in which different mathematicians approach the otherwise difficult to quantify value of an entity — any entity.
College football is a wonderful example of fluid entities that change in time for a variety of measurable reasons: coaching changes, talent levels, the strength that comes to a program from simply winning, and the negative impact of constantly losing. Football Teams are an assemblage of minds and bodies for the purpose of achieving victory. However every team is different. Every team changes in time.
In my roughly three decades of watching, analyzing and evaluating football; after the many interviews with coaches, players, and other writers or analysts, I’ve come to one conclusion. The MAXIMUM level of performance any team is likely to achieve can usually be predicted and is based on the cumulative value of all related EXPERIENCE factors.
That performance quotient or predictor is quantified as a projected WON/LOSS record. In other words, the value and weight associated with the EXPERIENCE LEVEL of all components that comprise a team will ultimately predict it’s season outcome.
Of course, the use of the variables that comprise the algorithm predictor for “experience” are not going to be an individually accurate for every single team over the course of the year. Some teams will suffer inconsequential losses of personnel while others will suffer devastating losses of key players, or team members. The death of a key player’s family member may not be predictable, but a negative can affect a team and change anticipated high outcomes. Those predictors are difficult to quantify because they are very random.
Suffice to say, some teams ranked very high, may be over-ranked based on their overall experience rating going into a season. Some teams may be ranked way too low based on the same factors.
The purpose of this exercise was to look at the experience factors under the weight of each added together and then used to devise logical EXPERIENCE VARIABLES from which to construct a rating system:
2012 NCAA Team Experience Ratings — see the results (click on the Experience Ratings again on the next page when it opens)
V1. Returning Starters divided by 22 = a quotient – The higher this number, the stronger a starting lineup a team has going into a season.
V2. Returning Letterman = a solid variable. ALL teams usually lose a few players from this total between spring and fall for various reasons. Inasmuch as that is universally accepted to be commonly the same for all teams, the loss is ignored in the evaluation process. Keep in mind, the loss of 3 players during that time frame for a school that has 50 returning lettermen is far less impactful than it would be to a team that only returns 35 lettermen.
V3. Returning Quarterback – If yes, the Returning QB is worth a factor of +10 in the algorithm. Without a starting QB, teams are much more likely to struggle. Rarely does a new starting quarterback come into a system and have an immediate positive impact. The learning curve is steep at the major college level and so the new quarterback will result in a negative impact = -10 points.
V4. Winning is important. Teams that experience the thrill of competing and winning get a solid mental mindset for winning in the future. Winning in the recent past can provide a confidence level that is translated into play on the field. A winning history can become a powerful force for predicting how a team views its aggregated capability.
The win/loss aspect of the predictor directly relates to the collective psyche of an organization. You win most often because you expect to win. The positive value of a winning tradition in the prior year is worth twice its face value as a positive number. A ten win team is therefore worth 20 experience points to the plus side.
V5. Losing is predictive and more devastating to team psyche. When teams perpetually lose, they fail to connect their effort with progress that can eventually lead to winning. Negative thought is significantly more powerful to persuade failure, than positive mental attitude is to lifting a team to wins.
Losses can result from lots of losses, not just on the field, but the losses that also result from individual failures, injuries, and arrests, anything that can instill in an organization a sense of failure. A negative loss number is worth three times its face value. So the 10 win season that lost three regular season games loses 9 basis points. The sum of V4 + V5 for a 10-3 Team is equal to +20-9 = 11 positive points. That is a solid predictor that a team will do well simply because it’s prior year was successful.
V6. Talent matters. – Upper echelon BCS and BCS like programs (Note Dame, BYU, Boise State) have talent, some more, some less, but these teams do rise to the top. Talent can be defined as the developed potential of the recruiting classes. It’s futile to use Scout, Rivals or other so called scouting services because they do not really evaluate all incoming FBS level talent.
We won’t get into that in this article, but suffice to say, the teams with great talent do usually succeed. But not always! UCLA , Texas and Notre Dame have for example done far less with great talent in the last decade than say TCU, Boise State and Georgia Tech with purportedly less talent.
A Talent quotient is important, but should not be over-valued. A strong experienced team with less purported talent can lay the wood to the team that is supposedly a lot more talented (BSU demolishing Georgia and Virginia Tech) in recent seasons is a perfect example, or Utah over Alabama in the Sugar Bowl a few years ago.
V7. Coaching and Staff experience is huge – The more experienced a staff is, the more it can relate to the experienced players. New staffs are even occasionally resented and so it takes time for a staff to mesh with a group of players. Staffs change for many reasons. The point is, the value of staff experience is more important than talent.
V8. Prior Year Schedule – The stronger a schedule, the more valuable the success experience. This factor should not however be over-valued. But there is no question that LSU gains a great deal of strength because it returns a lot of players that experienced success against an SEC schedule as well as an out-of-Ccnference (OOC) schedule that included Oregon and West Virginia. When a team has success against really good competition, it strengthens the overall experience value for returning starters and back-ups who have lettered.
V9. Regardless of all other factors, teams must play a new schedule. Playing at home a lot means more to experienced teams. Couple experience with home games and you get an environment that is guaranteed to promote success. One more home game can often offset the loss of one key player. Therefore, it is important to factor in the current schedule to the extent a team gets to appear before the home crowd. A six game home schedule is neutral because the team must also travel six times. A seven game home schedule is worth two extra starters. An eight game home schedule is worth 4 extra starters. Home games really are that valuable.
I did not factor in the value of where teams travel. It should be noted that teams which do NOT travel very far for their few home games are often over-valued or given too much credit for schedule difficulty. While I did not take those factors into account, it is meaningful psychologically and physically on members of the traveling team.
Alabama will only play four road games and none are more than a long bus trip or hour plane flight away. Like Alabama; LSU will never leave the south, never play at altitude, it will never experience driving snow or the bitter cold. We think of LSU and Alabama as power teams, but in some ways, they and other SEC teams almost never prove themselves the way Michigan, Nebraska, Ohio State, BYU, UTAH or Wisconsin do in late November.
They will not be tested playing at nearly a mile above sea level where the oxygen content of the air is 14% lower than at sea level. They experience humid heat, but do not know the difficulty of desert; an oven-like dehydration and 110 degree heat. We think of SEC teams as being formidable, but they really play most games in friendly venues or travel very little, and rarely experience some of the more difficult natural elements of weather and geography that other teams see year in and year out.
With that, I constructed the algorithm to produce a new ranking based on the revised W/L record predictor. The formula weighs all factors appropriately as follows:
[(V1/22 x V2) + V3 + (V4 x 2) + (V5 x 3) + (V6 x 3) + (V7 x 4) + V8 + v9] = ΣtER [Total Experience Rating} = ΣtER /12 (games) = WP (Win Predictor) Note: teams with a 13th game at Hawaii were not adjusted in.
This number is rounded to the nearest whole integer because you cannot really win a fraction of a game. The number of predicted Regular Season Losses is the difference between 12 – WP. Teams with the same W/L record are ranked on the basis of Score such that the team nearest the beginning of that WIN LEVEL is most likely to win that many games, and the one at the bottom is least likely in the group.
All things being equal, the one closest to the top of a WIN LEVEL is also more likely to over-achieve and win more than predicted, and the one at the bottom is most likely to win less. Notice that the Predictor Record W/L percentage is not exactly based on the whole integer .pct. This value is based on the projected non-rounded record so that each team can be sorted based on which is by record more likely to hit that projected value and which is less likely.
Also, it is important to realize that a predictor is based on the best case result for any given team. Again, any significant personnel losses can change the outcome. Inasmuch as those are difficult to predict from one team to another, the reality cannot be predicted. Utah returns a starting QB in Jordan Wynn.
But if he’s lost due to an injury, the highly regarded UTES that rank 10th in the “revised expectation” would drive a team from +5 to -5 in the QB column. That would alter UTAH’s score from 117.648 and a 10-2 regular season projected record to 102.648 and a 9-3 record.
Some Surprising Results
With that, we notice some immediate trends. Some teams that are projected to finish very high by pollsters do not project as strong based on the overall experience factor.
USC is loaded with talent and has a plethora of returning starters, and a Heisman Candidate at Quarterback, but is thin. Years of probation have left the Trojans with fewer experienced athletes behind the front line starters and that will haunt the Trojans as they get into the meat of their very difficult schedule.
They are ranked #1 in the Polls, but the experience predictor says they will do no better than 9-3 in the regular season. A 9-3 record is still an excellent season but Stanford, Oregon and Utah are all significantly deeper and have better depth and even more highly valued experienced coaching staffs AT THIS TIME.
In the National Picture, Florida State is by experience and talent, right in the middle of the mix. Teams that did not fare as well, which surprised me, included Alabama and LSU. Both fell to 8th and 17th respectively. Alabama returns a starter at QB and LSU does not.
BYU’s Experience Moves the Cougars Up
Oklahoma comes on strong in the Big-12 as does Georgia and Arkansas in the SEC. One team that is way undervalued besides Utah is their arch rival Brigham Young. The Cougars ended the spring with a whopping 65 lettermen and 14 solid returning starters. Not factored into this is the consideration of BYU’s average age for athletes that is nearly one year senior to most other teams because of LDS Missions.
Missions are problematic in that athletes often lose their physical and competitive edge while away from the game. But BYU gains in exchange considerable mental maturing in many cases. It’s probably a wash but the mental side of the game is a big plus.
BYU is simply mature, experienced and undervalued based on that. Yes, it has one of the more difficult road schedules in the country, but the team experience and talent is probably greater than at any time in its storied history. Look for BYU to have a possible BCS busting year.
Other teams that should have big years include Nebraska, Texas and Michigan. Surprise teams like BYU include Tennessee, Texas Tech and Georgia Tech. Greatly overvalued teams besides USC include West Virginia, Michigan State, and South Carolina.
Again, as with any algorithm, it cannot predict the unpredictable. Furthermore these are best record scenarios. It is easier for teams to under perform than over perform. In the end, what matters is what transpires on the field. What this measurement does is to value teams realistically based on their overall Experience factor and presents a snapshot before the season begins that mirrors what SHOULD BE the W/L record of teams by the end of the regular season.