IN THE UNITED STATES DISTRICT COURT CASE #

PLAINTIFF: Clifford Ray Hackett Gen Del Tumon GU 96911

DEFENDANT: KEI 674A South Marine DriveTamuning, GU 96913

MODIFIED COMPLAINT

1. This Court has subject matter jurisdiction under 28 U.S.C. sections 1331 and 1343.
2. This action is commenced pursuant to 2201 and 2202 and 42 U.S.C., section 1983.

GENERAL ALLEGATIONS

3. Plaintiffs are citizen of the United States of America.
4. Defendant owns a commercial business
5. Plaintiffs are individuals with disabilities. Defendant’s business blocks disabled persons.
6. On July 26, 1990, Congress enacted the ADA, 42 U.S.C. section 12101, et seq.
7. The Congressional findings include: “discrimination persists "42 U.S.C. section l2101(a).
8. Congress says ADA is to: mandate “standards” 42 U.S.C. section 12101(b).
9. Congress gave time to implement the Act. The effective date was January 26, 1992.
10. Defendant’s business has barriers that block disabled persons.
11. ADA Title III, covers "Public Accommodations and Services." 42 U.S.C. section 12181.
12. Congress included businesses as public accommodations. 42 U.S.C. section 12181.
13. Defendant’s business is discriminating by not ADDING CURB RAMPS
14 The barrier removal is.“readily achievable” 42 U.S.C.. section 12182(b)(2)(A)(iv).
15. Readily achievable removing barriers 28 C.F.R.. section 36.304(a) – (c).
16. Similar businesses have made similar modifications, but defendant chose not to comply.
17. In Section 44 and 190 of the IRS Code, businesses get a tax credit of 50% of the cost
18. Plaintiff wants to use the defendant’s business.

CLAIM FOR RELIEF:

19. Pursuant to the ADA, 42 U.S.C. section 12101, et seq., and law pursuant to this Act, 28 C.F.R. §36.304, defendant was to remove barriers by January 26, 1992. Defendant has not.
20. By failing to remove barriers defendant violates the ADA.
WHEREFORE, the plaintiffs pray that the Court order the defendant to remove barriers BY ADDING CURB RAMPS and pay plaintiffs attorney fees and such relief as may be just, proper, and equitable.

I HEREBY CERTIFY that a true copy of the foregoing was sent to the other party the same day it was sent to this court.


Metin’s Media & Math
How To Calculate the Elo-Rating (including Examples)

 allmediamath
4 years ago
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In sports, most notably in chess, baseball and basketball, the Elo-rating system is used to rank players. The rating is also helpful in deducing win probabilities (see my blog post Elo-Rating and Win Probability for more details on that). Suppose two players or teams with the current ratings r(1) and r(2) compete in a match. What will be their updated rating r'(1) and r'(2) after said match? Let’s do this step by step, first in general terms and then in a numerical example.

The first step is to compute the transformed rating for each player or team:

R(1) = 10r(1)/400

R(2) = 10r(2)/400

This is just to simplify the further computations. In the second step we calculate the expected score for each player:

E(1) = R(1) / (R(1) + R(2))

E(2) = R(2) / (R(1) + R(2))

Now we wait for the match to finish and set the actual score in the third step:

S(1) = 1 if player 1 wins / 0.5 if draw / 0 if player 2 wins

S(2) = 0 if player 1 wins / 0.5 if draw / 1 if player 2 wins

Now we can put it all together and in a fourth step find out the updated Elo-rating for each player:

r'(1) = r(1) + K * (S(1) – E(1))

r'(2) = r(2) + K * (S(2) – E(2))

What about the K that suddenly popped up? This is called the K-factor and basically a measure of how strong a match will impact the players’ ratings. If you set K too low the ratings will hardly be impacted by the matches and very stable ratings (too stable) will occur. On the other hand, if you set it too high, the ratings will fluctuate wildly according to the current performance. Different organizations use different K-factors, there’s no universally accepted value. In chess the ICC uses a value of K = 32. Other approaches can be found here.

—————————————–

Now let’s do an example. We’ll adopt the value K = 32. Two chess players rated r(1) = 2400 and r(2) = 2000 (so player 2 is the underdog) compete in a single match. What will be the resulting rating if player 1 wins as expected? Let’s see. Here are the transformed ratings:

R(1) = 102400/400 = 1.000.000

R(2) = 102000/400 = 100.000

Onto the expected score for each player:

E(1) = 1.000.000 / (1.000.000 + 100.000) = 0.91

E(2) = 100.000 / (1.000.000 + 100.000) = 0.09

This is the actual score if player 1 wins:

S(1) = 1

S(2) = 0

Now we find out the updated Elo-rating:

r'(1) = 2400 + 32 * (1 – 0.91) = 2403

r'(2) = 2000 + 32 * (0 – 0.09) = 1997

Wow, that’s boring, the rating hardly changed. But this makes sense. By player 1 winning, both players performed according to their ratings. So no need for any significant changes.

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What if player 2 won instead? Well, we don’t need to recalculate the transformed ratings and expected scores, these remain the same. However, this is now the actual score for the match:

S(1) = 0

S(2) = 1

Now onto the updated Elo-rating:

r'(1) = 2400 + 32 * (0 – 0.91) = 2371

r'(2) = 2000 + 32 * (1 – 0.09) = 2029

This time the rating changed much more strongly.

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Categories: Education, Mathematics, Science, Statistics
Tags: calculate, Chess, elo, example, game, ranking, rating, sports
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