Sports Equipment For Back To School

Autumn means back to school, and the start of a whole new afterschool sports season for the kids.

That means we parents probably need to purchase new sporting equipment for kids, just like all the other back-to-school supplies we've been buying.

ShopWiki, though, could make it easier on you. It advertises itself as similar to Google for online shopping, so that it searches all the best deals on the Internet, not just the stores that pay for placement on the site. That means it's a source for a variety of sports gear, which includes what ShopWiki terms individual sports which covers such diverse activities as dance and inline skating.

The other thing I find really useful about ShopWiki is not only does it offer products, but it provides information on the different categories through its online buying guide.

And, of course, buying over the Web provides the convenience of anytime shopping, and having all of that gear delivered straight to your door.

This was a sponsored post.

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Computer System Consistently Makes Most Accurate NCAA Picks

Sports professionals and fans get pretty emotional about their picks for the NCAA basketball tournament each year, and that emotion often clouds their judgment.

But three engineering professors at the Georgia Institute of Technology have created a computer ranking system, called LRMC, that consistently predicts NCAA basketball rankings more accurately than the AP poll of sportswriters and the ESPN/USA Today poll of coaches, formulas (the Ratings Percentage Index), other computer models (the Massey ratings and the Sagarin ratings), and even the tournament seeds themselves.

After correctly picking all four of this year's finalists, the LRMC method has now identified 30 of the last 36 Final Four participants (83 percent accuracy over the past nine years of NCAA tournaments) as one of the top two teams in their region. Over the same nine-year stretch, the seedings and polls have correctly identified only 23, and the RPI identified 21.

LRMC (Logistic Regression Markov Chain) is a college basketball rankings system designed to use only basic scoreboard data, including which teams played, which team had home court advantage and the margin of victory. It was originally designed by Joel Sokol and Paul Kvam and has been maintained and improved by Sokol and George Nemhauser, all three optimization and statistics professors in the Stewart School of Industrial and Systems Engineering at Georgia Tech.

"As fans, we only get to see most tournament teams two or three times at most during the season, so our gut feelings about a team are really colored by how well or poorly they played the few times we've been watching," says Sokol. "On the other hand, our system objectively measures each team's performance in every game it plays, and mathematically balances
all of those outcomes to determine an overall ranking."

LRMC seems to have a particular knack for predicting good bubble teams and identifying the top teams. In addition to correctly picking the Final Four, LRMC also correctly identified several over-rated and under-rated teams as potential upsets. First-round losers Drake (5-seed, LRMC #30), Vanderbilt (4-seed, LRMC #38), and Connecticut (4-seed, LRMC #26), as well
as second-round loser Georgetown (2-seed, LRMC #12), were all picked by LRMC as significantly over-rated teams.

On the other hand, teams like West Virginia (7-seed, LRMC #17), which defeated second-seeded Duke, and Kansas State (11-seed, LRMC #19), which defeated sixth-seeded USC, were correctly identified by LRMC as under-rated teams that could pull off one or more upsets.

But LRMC isn't perfect -- it picked Clemson as under-rated (upset in the first round) and Davidson wasn't identified as under-rated by any major ranking method, including LRMC.

LRMC differs from other computer rankings systems in two important ways. When determining the value of home court advantage, LRMC considers how much playing at home helps a team win rather than how many points playing on a home court is worth.

Georgia Tech researchers have also been able to show that very close games are often "toss-ups," meaning the better team barely wins more than half the time. So, they determined that winning a close game shouldn't be worth as much as winning easily, and losing a close game shouldn't hurt a team's ranking as much as losing badly. LRMC's ranking methodology takes
this into account.

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Medalist: Global Warming Hurts Skiing

Rising temperatures due to global climate change pose dangers to the sport of skiing, according to an American Olympic medalist.

"My parents put me on skis as soon as I could walk and I never looked back. But I worry that in the future the only place you'll find skiing is in the history books," says Ted Ligety, medalist at the 2006 Winter Olympics in Turin, Italy.

"Global warming has become a major threat to the sport. I've already seen changes in my lifetime," he adds. "It seems warmer, and there's been less snow over the years. The ski season is starting later and ending earlier. Scientists say that the ski season could actually keep shrinking until there's never enough snow to ski, regardless of the time of year.

"But it doesn't have to be that way," he says. "I'm teaming up with World Wildlife Fund to bring attention to this problem and encourage people to save skiing. The most important thing people can do is to tell politicians to make global warming a priority. And everyone can make a difference by saving energy at home and using less gas. Whether it's buying money-saving energy efficient bulbs or driving a hybrid, every bit counts."



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