Some Blog Site

I ran a few different races this year – a 5k, a 10k, and a couple half marathons. Here are various thoughts from those events.

1. After running a few races, you get to see similar signs. By “signs” I’m referring to the notes of encouragement written by spectators and held by them during the race for the runners to view. A number of the signs are meant to be humorous, but the same jokes gets old quickly. This year’s notable entries (i.e. they made me laugh or at least smile, as opposed to the familiar signs that elicit no reaction anymore) were
You’re running better than the government.
and
Run like you just fumbled a punt!

For those of you who are wondering about the last sign – the run was the day after the Michigan-Michigan State football game, a significant rivalry which Michigan lost this year in the last 10 seconds by fumbling a punt.

2. When the Detroit Marathon website and email suggest you get there early (before 6:00AM) to avoid traffic problems – take them seriously. I took the route I normally take for major sporting events, but I was late because traffic for the marathon is much, much worse than it is for a Lions or Tigers game.

The race started at 7:00, and I couldn’t get to a parking spot until 7:15. Then I had to walk about 5 blocks to the starting line. When I was about a block away, I could see people crossing the starting line and I heard the announcer over the loudspaker start counting down “30 seconds”. The material they had sent out before the race informed everyone that once the last group has started, they close the starting line and no one else is allowed to start.

So I heard the countdown “30 seconds … 10, 9, 8, 7, 6, 5, 4, 3, 2, 1” and I’m a block away so I figured there’s no way I’m going to make it. But the mass of people continued to go through the starting line. I got closer and saw a ton of people (actually many tons of people) still in line. And then I heard the announcer start counting again.

It turned out he counted down to the start of each wave. I was supposed to be wave C, but I ended up in wave M. And there were more waves after that. It took about a half hour just to get everyone through the starting line. I suppose it is an interesting logistics problem – how to most efficiently get 27,000 people through a gate that is about 3 lanes wide on the road.

3. Don’t be late for a major city marathon or half marathon if you care about your time. If you don’t start with your group of people with similar paces, you will be stuck in the group of people with slower paces. This is a problem in big city races because there are tens of thousand of people in the race and if you are stuck in a slow pack, you are stuck. The people are packed curb-to-curb and there is no picking your way through the people like there is in smaller races.

The slower group was a relatively happy, festive group. And I witnessed something I never would have seen if I were in the faster group – the in-race selfie. People were stopping and taking photos of themselves at various landmarks. The most popular one was the Ambassador Bridge to cross into Canada. At first I thought it was amusing. But then some people in front of me stopped for that and I had to dodge them. A word of advice: if you have to (or want to) stop during a race, move off the side of the course first, so you don’t block the people behind you. And in this race, there were literally thousands of people behind them.

4. My time was slower than expected for the Detroit race. Much of that was due to the fact that I was stuck in a slow pack for a while, but some of it is because I ran a longer route than necessary. When there were breaks in the pack, I ran faster, but I had to weave around clumps of people. On the Ambassador Bridge, in particular, I probably did as much running side to side to find a break in the wall of people as I did running forward. The extra distance adds up.

With college football underway, there are plenty o’ pundits weighing in on who is the best.

It’s a perennial conversation because of the way that the NCAA has avoided finding a champion for college football. Instead, we have many winners. Everyone gets a bowl game!

But I digress.

My topic today is how I disagree with those who say that the previous year’s champion should remain the top-ranked team the next year until they lose.

I have a couple of problems with that.

1. The teams are not the same from year to year. The coach might be, and many of the players are, but it is not the same team. It’s college, so you should expect around 25% turnover each year.

2. That statement of “you’re the best until you lose” applies only to king of the hill and boxing. Unless you have to defend your title against challengers, you can’t claim to still be the best. Or have others claim it for you.

Last year’s champion should not be assumed to be the best this year. If you’re putting together a ranking, evaluate each team based on this year’s accomplishments and statistics.

Or for those who want to have a reason for pre-season rankings: you could propose a king-of-the-hill format. No more predictable scheduling – team #1 plays team #2 and whoever wins gets to be team #1 the next week. The loser is out of the running.

Let’s assume 128 teams total, so 64 matchups each week.
1 vs 2, 3 vs 4, 5 vs 6, 7 vs 8, etc.
1v2 winner becomes #1, loser becomes #65
3v4 winner becomes #2, loser becomes #66
5v6 winner becomes #3, loser becomes #67
7v8 winner becomes #4, loser becomes #68
.
.
.
63v64 winner becomes #32, loser becomes #96
.
.
.
127v128 winner becomes #64, loser becomes #128

So the formula is
For n teams, your new ranking is
if you win: r = rp/2
if you lose: r = rp/2 + n/2
where r is your new ranking and rp is your previous ranking

That way, if you win all your games you can stay #1.
If you lose a game, it will take you log2(r)+1 games to get back to #1.

This method would not be good for relative rankings late in the season, or at the end of the season. But if you want a method for defending the title, you can’t be concerned with the losers.

As I was listening to a pre-game interview on the radio recently, I was interested to hear all the questions coming from the reporter guy.

• So-and-so had a good game last time against this opponent. Talk about him.
• The team is 3 games behind the leader in this division. Talk about the importance of this game.

I’ve written about the issue of sports reporters and the use of “talk about” before.

Since I can’t beat them, however, I will join them. Rather than trying to get reporters to ask real questions, I now suggest replacing them with a robot. Simple natural-language AI that starts each statement with “Talk about” and then adds some keywords relevant to the team and the schedule and the players – that should be indistinguishable from the current situation.

Think of it as a reverse Turing test. If the audio clips are so meaningless/predictable/boring that they could be replaced by a computer and no one would notice, then why hire reporters to ask the questions?

Also, the answers the coaches/managers/players give can be just as bland:

• I thought everyone played well individually today, we just didn’t play as a team
• We had a good game plan, we just didn’t execute well
• I was glad to be able to do my part to help the team get the win today
• We had some trouble early, but everyone pulled together and it showed in the second half

If the AI is good enough, it could replace both sides of the interview and neither reporters nor sports figures would need to be bothered.

The only problem would be the audio itself – getting the computer to sound like the actual person. Maybe have the computer generate the scripts, hire a couple of voice actors and they can read the scripts and produce the interviews for the whole league in one batch.

I’ve also come to view pre-game and post-game interviews as the equivalent of comments sections of news sites: they’re going to have them, but I know they’re a waste of time so I avoid/ignore them.

They were the police, the jury, and the executioner all in one.

They were The Commissioners.

So maybe he’s not the executioner. But he will approve your sentence and then hear your appeal and decide if he agrees with himself or with you.

In the last week, we had the announcement of the 2015 NFL schedule. Now that we know who will play whom and when, we can start predicting wins and losses.

I keep my predictions over at Some Fun Site. View results of previous football seasons.

2014 Summary

Last year, I predicted that

• Cleveland Browns = 4-12
• Detroit Lions = 10-6
• New York Giants = 5-11
• Pittsburgh Steelers = 10-6

How they actually did was

• Cleveland Browns = 7-9
• Detroit Lions = 11-5
• New York Giants = 6-10
• Pittsburgh Steelers = 11-5

Pretty good, except for the Browns.

(more…)

Everyone familiar with timed sports (football, basketball, hockey, etc.) knows that the last minute of the game lasts a lot longer than the first minute of the game.

But how bad is it? And how does it change during the game?

I thought I would put together a chart showing the concept. I don’t have any actual data – I’m just going off my instinct here.

Maybe the chart is skewed toward the worst case, not average. For example, how long – real time – does the last 10 seconds of a close NBA game take? How many plays can occur in the last 15 seconds of an NFL game if a team is trying to rally a win? That is what I was thinking when I chose the Y-axis scale of multiples of game time. If 15 seconds of the game clock takes 2 minutes of my life, that’s a scale of 8x.

One of the more annoying aspects of sports is the delay that is part of the game but shouldn’t be. Example #1: intentional fouling near the end of a basketball game. Example #2: trying to ice the kicker for field goals in a football game. I hope at some point they change the rules to forbid those.

Any recommendations for the Y-axis scale?
Any other proposed changes to the rules to make the ends of games less annoying to the fans?

Okay, okay, it is 2015 at this point, but the results are headlines as 2014 because they match with the 2014 predictions made in 2014 for the 2014 season. Also, the results are not all-haiku, just the predictions were. A more accurate title would be “Results for the All-Haiku Predictions made in 2014”.

Before the bowl games commenced for this past college football season, I made some predictions. Here, for your reading enjoyment, is the tally of those predictions. Note that the results are not in haiku form, in contrast to the predictions.

Results

Here is the list (correct predictions in green, incorrect in red):

Nevada over ULL

Utah State over UTEP

Colorado State over Utah

Air Force over Western Michigan

South Alabama over Bowling Green

Memphis over BYU

Marshall over N. Illinois

San Diego State over Navy

Western Kentucky over Central Michigan

Rice over Fresno State

Louisiana Tech over Illinois

Rutgers over North Carolina

NC State over UCF

Virginia Tech over Cincinnati

Arizona State over Duke

Miami over South Carolina

Boston College over Penn State

USC over Nebraska

West Virginia over Texas A&M

Oklahoma over Clemson

Arkansas over Texas

LSU over Notre Dame

Georgia over Louisville

Stanford over Maryland

TCU over Ole Miss

Boise State over Arizona

Mississippi State over Georgia Tech

Auburn over Wisconsin

Baylor over Michigan State

Missouri over Minnesota

Oregon over Florida State

Alabama over Ohio State

Pittsburgh over Houston

Iowa over Tennessee

Kansas State over UCLA

Washington over Oklahoma State

Florida over East Carolina

Toledo over Arkansas State

Oregon over Alabama

And here are the results of the various forecasting methodologies (see the first year for description of the methodologies) (also, use the word methodologies if you want to sound important; methods would work just as well and is shorter) :

• Some Blog Site picks were 21-18 (similar to last year)
• CBS120 picks were 22-17
• HTW was 22-17 for the official Home Team Wins (HTW)
• HTW was 25-14 for the Geographical Home Team (GHT)
• Isaacson-Tarbell Postulate (ITP) was 22-17 if using HTW
• ITP was 23-16 if using GHT

I won’t analyze the results as much as I did last year, mainly because I had more time and more sleep last year. But it was a good year for all predictors – every method was over 50%. I just need to figure how to better predict outcomes. Especially against the spread.

Because of the disparities between institutions and between conferences, it is tougher to predict bowl games than NFL games. But one thing seems to be pretty consistent: if you don’t want to do a lot of research, just pick the team whose campus is closer to the bowl game.

Thoughts on the season’s results

I bet TCU agrees with me that an 8-team playoff would be an improvement on the current format of 4 teams. But 4 teams is better than the previous format of 2.

Conferences

Since the strength of the conference has something to do with the results, I thought I would tally each conference’s bowl game record for the 2014 (and the first bit of 2015) season.

• AAC: 2-3
• ACC: 4-7
• Big 10: 6-5*
• Big 12: 2-5
• Independent: 2-1
• MAC: 2-3
• MW: 3-4
• PAC12: 6-3*
• SEC: 7-5
• Sun Belt: 1-2
• USA: 4-1

* additional win/loss due to playoff + championship game

So the best conference was Conference USA (they won 83% of their bowl games) and the worst was the Big 12 (at 29%).

Or maybe the Sun Belt is the worst conference because they sent the fewest teams to bowl games again this year.

Perhaps you could say that the SEC was the best because they had 12 teams go to bowls. Or you could say they were just the most popular conference.

Perhaps you could say that the Big Ten was the best because they won the championship. Or maybe the SEC is the best because they won the most bowls (at 7).

Next year: playoffs again! TCU and I will be awaiting the expansion of the format to 8 teams. It might be too early to tell with only one sample year, but 8 seems to be the most reasonable number to whittle down 120 teams to a champion.