Broodmare Statistics and a Filly to Watch – BloodHorse.com

Broodmare Statistics and a Filly to Watch - BloodHorse.com nevin manimala
Broodmare Statistics and a Filly to Watch - BloodHorse.com nevin manimala

By J. Keeler Johnson (“Keelerman”) Twitter: @J_Keelerman

With the 2019 Triple Crown in our rearview mirrors, I thought we’d kick off the second half of the racing season with a different type of post than usual.

Summer is just around the corner, and Saratoga is less than a month away, so the focus of racing fans will soon turn to juvenile racing as well-bred youngsters with high-profile connections hit the track and take aim at the 2019 Breeders’ Cup Juvenile and 2020 Triple Crown.

Therefore, now seems like as good a time as any to address a very old question—are foals produced by graded stakes-winning mares more or less likely to win stakes races than foals produced by less accomplished mares?

Old-time breeders used to believe high-class, heavily-raced mares were less likely to produce stakes-winning foals than mares who accomplished little or nothing on the track. And while we see no shortage of stakes winners produced by unraced or unaccomplished mares, isn’t that to be expected since they greatly outnumber graded stakes-winning mares?

The May 18, 2019 edition of BloodHorse magazine included a BloodHorse MarketWatch section that might be of interest to handicappers. An article by Nicole Stafford included several charts highlighting the produce records of “all mares that produced North American foals from 2000-07,” sorting the broodmares into categories based on their racing accomplishments and detailing the percentage of their foals that achieved stakes-caliber racing class.

The results are eye-opening. The main takeaway? Graded stakes-winning mares produce significantly higher percentages of stakes winners (8.2%), graded stakes winners (4.1%), and Grade 1 winners (1.4%) than all other categories of mares, including stakes-winning mares, which produce 6.2% stakes winners, 2.2% graded stakes winners, and 0.6% Grade 1 stakes winners.

Further down the list, unraced mares produce 3.6% stakes winners, 1.0% graded stakes winners, and 0.3% Grade 1 winners. Mares who won at least one race (but never placed at the stakes level) fare slightly worse, producing 3.2% stakes winners, 0.8% graded stakes winners, and 0.2% Grade 1 winners.

To put it another way, graded stakes-winning mares are approximately 5-7 times more likely to produce a Grade 1 winner than mares who never won or placed at the stakes level. Even stakes-winning mares are 2-3 times more likely to produce Grade 1 winners than those less accomplished mares.

Furthermore, if a graded stakes-winning mare is a half or full sibling to another graded stakes winner or a mare that produced a graded stakes winner, the stats are even more impressive. Mares that fit this category produce approximately 9.5% stakes winners, 5.2% graded stakes winners, and 1.7% Grade 1 winners.

You can argue the reasons behind these stats all day long. Superior genetics surely plays a factor, along with the fact that graded stakes-winning mares are much more likely to be bred to a top stallion than unaccomplished mares.

But if you see a two-year-old out of your favorite graded stakes-winning mare hit the track this summer, you can rest assured this well-bred youngster has a significantly better chance than most runners to become a stakes winner.

One Play for Stephen Foster Day

The Stephen Foster Handicap (G2) might be the highlight of the stakes-packed June 15 card at Churchill Downs, but the horse I’m most excited to play is running in the Regret Stakes (G3), a 1 1/8-mile turf test for three-year-old fillies.

#4 Varenka held her own against high-class company as a maiden last year, finishing second behind Newspaperofrecord in the Miss Grillo Stakes (G2) and fifth behind that same rival in the Breeders’ Cup Juvenile Fillies Turf (G1), where she was beaten just 1 ½ lengths for second place.

Trained by Graham Motion, Varenka took the winter off, but returned to win a 1 1/16-mile maiden special weight on May 11 at Belmont Park with a terrific rally from off the pace. The early fractions were pedestrian (:25.04, :50.47, and 1:15.28), yet Varenka managed to pass six horses in the final five-sixteenths of a mile, blazing the distance in :27.99 per Trakus to win going away by two lengths.

This was a remarkable performance considering that the race was otherwise dominated by speed horses and was assigned a speed-favoring Closer Favorability Ratio (CFR) of 9 (on a 1-to-100 scale) by RacingFlow.com. An exceptional effort is required for late runners to win races like these, so I give Varenka a lot of credit for overcoming this unfavorable scenario.

As a daughter of Ghostzapper out of a Dynaformer mare, Varenka should relish the opportunity to stretch out to 1 1/8 miles in the Regret Stakes. I’m expecting her to unleash another powerful finish and win with ease on Saturday.

Now it’s your turn! Who do you like in the weekend stakes races?

*****

Want to test your handicapping skills against fellow Unlocking Winners readers? Check out the Unlocking Winners contests page—there’s a new challenge every week!

*****

J. Keeler Johnson (also known as “Keelerman”) is a writer, blogger, videographer, handicapper, and all-around horse racing enthusiast. A great fan of racing history, he considers Dr. Fager to be the greatest racehorse ever produced in America, but counts Zenyatta as his all-time favorite. He is the founder of the horse racing website www.theturfboard.com.

The human toll: People behind the statistics of meth in Montana – KULR-TV

The human toll: People behind the statistics of meth in Montana - KULR-TV nevin manimala
The human toll: People behind the statistics of meth in Montana - KULR-TV nevin manimala
...SIGNIFICANT WEATHER ADVISORY... AT 443 PM MDT, DOPPLER RADAR WAS TRACKING A STRONG THUNDERSTORM 19
MILES NORTH OF COLUMBUS, MOVING EAST AT 20 MPH. THE STORM MAY REACH
BILLINGS BY 5:30 PM MDT. HALF INCH HAIL AND WIND GUSTS UP TO 50 MPH ARE POSSIBLE WITH THIS
STORM. LOCATIONS IMPACTED INCLUDE...
HALFBREED LAKE WILDLIFE, ACTON, MOLT AND COMANCHE. PRECAUTIONARY/PREPAREDNESS ACTIONS... TORRENTIAL RAINFALL IS ALSO OCCURRING WITH THIS STORM, AND MAY LEAD
TO LOCALIZED FLOODING. DO NOT DRIVE YOUR VEHICLE THROUGH FLOODED
ROADWAYS. && 

Small farms produce more food than statistics show – Horizon magazine

Small farms produce more food than statistics show - Horizon magazine nevin manimala

While small farms can play an important role in supporting rural economies, many owners struggle to earn a decent living which can lead to a difficult choice – sell up or try to get more land to become commercially viable. This situation is helping drive up the average size of an EU farm.

According to Dr Teresa Pinto Correia, an expert in rural landscape management at Évora University in Portugal, if the true value of small farms was better understood then they could access more governmental and financial support.

‘Small farms are producing much more than what is in the statistics,’ she said. ‘In some cases the official statistics show (what is) close to the reality, but there are very clear cases showing that the production estimates of small farms are underestimated.’

These farms are an important way to contribute to the food system, she adds, because they support rural food security as well as shorter transport distances and more seasonable production which can have less negative environmental impacts.

Dr Correia is leading the SALSA project which found the production gap after analysing 800 small farms across 25 regions in the EU and 100 small farms across five regions in Africa. Dr Correia says the gap possibly comes from official statistics not accounting for food that is used on the farm to feed family, friends or animals. Food grown on farms often meet between 25% and 40% of that farm’s own requirements.

The farms in the study were mostly defined as those with less than five hectare operations and ranged from potato farmers in Poland to olive farmers in Greece and some growing a variety of crops in France, Portugal and Norway.

The team used satellite images to analyse the land and crop patterns of the small farms, and then interviewed farmers to get an overall estimate of the production capacity for a selected number of crops. They cross-checked figures with agricultural experts and finally compared numbers from each farm against official statistics of production from that region.

Small farms produce more food than statistics show - Horizon magazine nevin manimala

‘There are very clear cases showing that the production estimates of small farms are underestimated.’

Dr Teresa Pinto Correia, Évora University, Portugal

Small farms produce more food than statistics show - Horizon magazine nevin manimala

Paper trail

The production gap unearthed by SALSA could mean these small farms are being undervalued. Dr Correia says their unreported food does not create a paper trail and that means they are producing a lot more food than ends up in certain statistics.

For example, the EU categorises the size of farms on their economic value. They say that small and very small farms make up almost 70% of total farms, but account for only 5% of the total crops and livestock sold from them. The underestimation found by SALSA means the small farms they examined would be unfairly represented in these types of statistics and that could cause them to be undervalued by markets, banks and governments.

In general economic terms, the more produce a farm provides the more value it has to the market, which often means bigger operations can end up getting preferential treatment. Very large farms in the EU already produce 71% of standard agricultural output, but account for just 6.3% of total farms.

‘Small farms’ potential to place more products in the market or increase production is not considered by official (government) regimes which look more at much larger farms,’ said Dr Correia. ‘If there was more support for small farms they could have a much bigger role (in food production).’

Support

Later this year, SALSA will present their findings to the European Commission which is currently planning the direction of agricultural policies from 2020 onwards. Dr Correia hopes the project will help convince decision-makers to see small farms as more of a way to improve food security and the economic resilience of rural communities.

According to Alan Matthews, Professor Emeritus of European agricultural policy at Trinity College Dublin in Ireland, small farms can be productive and profitable when they specialise in quality produce. He is referring to enterprises such as vineyards, orchards or farms that add value to their produce – for example turning milk into ice cream, or fruits into jams.

While he acknowledges the important role small farms play in supporting rural households and their communities, he says the strong trend of people leaving agriculture for a better quality of life elsewhere seems to be an inevitable trend. 

‘In every country in the world over a long period of time, half a century or more, we observe people moving out of agriculture,’ he said.

For that reason, support should help those who want to set up or remain on small farms in the most productive and sustainable way, he says.

‘(Support) should go to small farms not because they are small but where they are better at managing the land (than bigger enterprises),’ said Prof. Matthews.

And small farms are not necessarily more environmentally friendly, particularly where there is a high concentration of livestock or if they run intensive horticulture or vineyard enterprises. And with one third of EU farmers over 65 years of age, the adoption of more modern sustainable practices and technology can be a challenge, especially if operating a less productive small farm, according to Prof. Matthews.

‘Their level of skills and education is simply not the same as some of these incomers that start small farms and seem to be able to make a great success at it,’ he said.

When it comes to food security, these farms play very different roles in different EU countries, says Prof. Matthews. For example, eastern and southern European small farms could play a bigger role in feeding households, he says, than those in the west which are often less diverse but plugged into the international cash crop markets. Any support would therefore need to account for a range of factors.

SALSA’s findings could lead to better technology and training assistance for small farmers, while policy could help create a more level playing field on the market.

But the big question, Prof. Matthews says, is how to best support every farmer to be economically and environmentally sustainable, regardless of their size.  

The research in this article was funded by the European Union. If you liked this article, please consider sharing it on social media.

Final area baseball statistics – Marion Star

Final area baseball statistics - Marion Star nevin manimala

Final Area Baseball Statistics

Batting Average

Player, School

Avg

Brock Stoner, Elg.

.623

Kwauve Booker, MH

.489

Evan Ulrich, BV

.474

Tate Tobin, High.

.453

Fletcher Holquist, BV

.444

Ben Atiyeh, BV

.433

Gabe Detwiler, MH

.418

Nate Blevins, Rid.

.413

Mason Kurtz, BV

.410

Clay Matthews, High.

.407

Trevyn Feasel, NU

.403

Connor Lust, Rid.

.390

Nico Wade, Card.

.390

Sam Leach, Rid.

.378

Wyatt Reeder, N’mor

.369

Mack Anglin, High.

.368

Joel Krebehenne, NU

.361

Talan Monticue, RV

.360

Avery Harper, Card.

.359

Hunter Mariotti, N’mor

.357

Jacob Hoffman, MH

.356

Reese Weissenfluh, High.

.355

Robert Curren, Ple.

.351

James Emery, RV

.351

Joey Hamon, MH

.348

Robert Curren, Ple.

.343

Brennon Newell, Ple.

.340

Owen Peters, Ple.

.337

Danny Vaught, Card.

.337

Aiden Davis, MH

.333

Bryce Lowry, Ple.

.333

Home Runs

Player, School

No

Mack Anglin, High.

5

Tate Tobin, High.

5

Kwauve Booker, MH

3

Gabe Detwiler, MH

2

Chris Bood, N’mor

1

Jonathon Curren, Ple.

1

Dakota Downard, N’mor

1

Kase Orahood, NU

1

Brayden Stark, Ple.

1

Brock Stoner, Elg.

1

Ryland Thomas, N’mor

1

Ashden Turrill, NU

1

Zach Wilson, MH

1

RBI

Player, School

No

Mack Anglin, High.

32

James Emery, RV

28

Kwauve Booker, MH

26

Fletcher Holquist, BV

24

Connor Lust, Rid.

24

Tate Tobin, High.

23

Gabe Detwiler, MH

22

Sam Mitchell, MH

22

Jaden Slone, RV

22

Mason Kurtz, BV

21

Nate Blevins, Rid.

20

Kolton Honeter, Card.

20

Sam Leach, Rid.

20

Nick McAvoy, Card.

20

Zane Dine, Ple.

19

Trevyn Feasel, NU

19

Trey Brininger, Card.

18

Reese Weissenfluf, High.

18

Clay Matthews, High.

17

Kase Orahood, NU

17

Brock Stoner, Elg.

17

Evan Ulrich, BV

17

Justin Hix, Elg.

16

Hunter Mariotti, N’mor

16

Runs

Player, School

No

Avery Harper, Card.

32

Brock Stoner, Elg.

30

Tate Tobin, High.

30

Gabe Detwiler, MH

29

Ethan Castle, RV

28

Nico Wade, Card.

27

Trey Brininger, Card.

26

Clayton Lust, Rid.

26

Kwauve Booker, MH

25

Sam Leach, Rid.

23

Nick McAvoy, Card.

22

Robert Curren, Ple.

21

Kolton Honeter, Card.

21

Connor Lust, Rid.

20

Talan Monticue, RV

20

Nate Blevins, Rid.

19

Aiden Davis, MH

19

Trevyn Feasel, NU

19

Joe Greene, Elg.

19

Danny Vaught, Card.

19

Reese Weissenfluh, High.

19

Justin Brown, Elg.

18

Bryce Lowry, Ple.

18

Clay Matthews, High.

18

Mack Anglin, High.

17

Anthony Padovano, NU

17

Adam Beard, Rid.

16

Carsen Newell, NU

16

Kase Orahood, NU

16

Owen Peters, Ple.

16

Ryland Thomas, N’mor

16

Steals

Player, School

No

Brock Stoner, Elg.

25

Justin Brown, Elg.

20

Nate Blevins, Rid.

19

Joe Greene, Elg.

16

Kwauve Booker, MH

15

Trevyn Feasel, NU

15

Trey Brininger, Card.

14

Robert Curren, Ple.

14

Gabe Detwiler, MH

14

Landon Mabe, Elg.

14

Nico Wade, Card.

14

Clayton Lust, Rid.

13

Nick McAvoy, Card.

13

Sam Leach, Rid.

12

Owen Peters, Ple.

12

Carter Andrich, Elg.

11

Aiden Davis, MH

11

Avery Harper, Card.

11

Wyatt Reeder, N’mor

11

Danny Vaught, Card.

11

Riley Conners, MG

10

Tate Tobin, High.

10

Reese Weissenfluh, High.

10

Wins

Player, School

No

Zach Ahern, NU

6

Sam Leach, Rid.

6

Tate Tobin, High.

6

Mack Anglin, High.

5

Ben Atiyeh, BV

5

Joey Hamon, MH

5

Devin McFarland, MH

5

Brennon Newell, Ple.

5

Kase Orahood, NU

5

Avery Harper, Card.

4

Kaden Lester, Rid.

4

Cam Miller, Elg.

4

Adam Beard, Rid.

3

Nate Blevins, Rid.

3

Chris Bood, N’mor

3

Trey Brininger, Card.

3

Devin Dillinger, MH

3

Kolton Honeter, Card.

3

Ben Mayse, RV

3

Micah Orr, NU

3

Nico Wade, Card.

3

Riley Young, NU

3

Strikeouts

Player, School

No

Brennon Newell, Ple.

83

Sam Leach, Rid.

68

Mack Anglin, High.

65

Devin Dillinger, MH

59

Nico Wade, Card.

55

Devin McFarland, MH

46

Kase Orahood, NU

46

Tate Tobin, High.

46

Joey Hamon, MH

45

Brayden Black, N’mor

44

Andy Anthony, BV

43

Avery Harper, Card.

40

Ben Atiyeh, BV

38

Kaden Lester, Rid.

38

Chris Bood, N’mor

29

Joel Butterman, MG

29

Brock Stoner, Elg.

29

James Emery, RV

28

Connor Wrentmore, RV

28

Riley Young, NU

27

Ben Mayse, RV

27

Robert Curren, Ple.

26

Zach Ahern, NU

24

Nate Blevins, Rid.

23

Cam Miller, Elg.

23

Owen Mott, High.

23

Adam Beard, Rid.

22

Riley Conners, MG

20

ERA

Player, School

Avg

Mack Anglin, High.

0.44

Mason Kurtz, BV

1.37

Zach Ahern, NU

1.56

Nate Blevins, Rid.

1.75

Brayden Stark, Ple.

1.78

Zach Mitchell, BV

1.80

Owen Mott, High.

1.83

Kaden Lester, Rid.

1.97

Adam Beard, Rid.

2.10

Jack Weaver, High.

2.10

Andy Anthony, BV

2.14

Tate Tobin, High.

2.17

Brennon Newell, Ple.

2.12

Connor Wrentmore, RV

2.24

Cam Miller, Elg.

2.31

Micah Orr, NU

2.41

Hunter Mariotti, N’mor

2.42

Ben Atiyeh, BV

2.48

Sam Leach, Rid.

2.51

Joey Hamon, MH

2.63

Devin McFarland, MH

2.68

Riley Young, NU

2.86

Ben Mayse, RV

2.90

Kase Orahood, NU

2.92

Chris Bood, N’mor

3.07

Brayden Black, N’mor

3.17

Mostyn Evans, N’mor

3.27

Kolton Honeter, Card.

3.29

Kwauve Booker, MH

3.15

Carter Sager, Elg.

3.39

Bryce Lowry, Ple.

3.50

Nico Wade, Card.

3.59

Joe Greene, Elg.

3.74

Avery Harper, Card.

3.80

Devin Dillinger, MH

3.85

Gabe Detwiler, MH

4.10

Robert Curren, Ple.

4.29

Brock Stoner, Elg.

4.99

Saves

Player, School

No

Owen Mott, High.

3

Riley Young, NU

3

Zach Ahern, NU

2

Kwauve Booker, MH

1

Bryce Lowry, Ple.

1

Cam Miller, Elg.

1

Turkey – Telecoms, Mobile and Broadband – Statistics and Analyses – PRNewswire

NEW YORK, June 10, 2019 /PRNewswire/ — While a number of political events led to the Turkish currency taking a recent slide; the telecoms sector itself has demonstrated keen growth over the past year or so with a rise in subscribers, data usage and uptake of bundled packages.

Read the full report: https://www.reportlinker.com/p05371616/?utm_source=PRN

Significant investments in expanding fibre-optic broadband networks are continuing while DSL retains it status of having the largest market share of all the fixed broadband access technologies. Turkey offers substantial opportunities for fixed broadband growth considering its current penetration is only around 15%.

4G LTE networks are well established across Turkey and are providing network coverage to around 87% of the population. In 2019 Turkcell, Turk Telekom and Vodafone Turkey continue to progress with their 5G developments.

A project is also underway in Turkey which is using the Universal Services Fund to supply mobile infrastructure to over 1,400 areas which are lacking.

Key developments:

Trade tensions with the USA led to Turkey placing a higher tariff on some imported goods coming from the USA, including a boycott on iPhones.
Turkey is proving to be one of the more progressive mobile markets in the Middle East with revenue from mobile data services growing quickly.

Interestingly, because of the three major mobile operators all launching 4G LTE services in April 2016; Turkey has witnessed a dramatic decline in 3G mobile subscriptions.

While mobile broadband becomes increasingly popular in Turkey – the fixed broadband network is also progressing sharply with a clear direction now towards fibre broadband.
Turkey also has excellent international infrastructure with links to many of international cable networks due to its geographic location between Europe and Asia.

Companies mentioned in this report include:

Turk Telekom (formerly Avea), Turkcell, Vodafone Turkey, Turksat, Superonline.

Read the full report: https://www.reportlinker.com/p05371616/?utm_source=PRN

About Reportlinker

ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need – instantly, in one place.

__________________________

Contact Clare: clare@reportlinker.com  

US: (339)-368-6001

Intl: +1 339-368-6001

SOURCE Reportlinker

Turkey - Telecoms, Mobile and Broadband - Statistics and Analyses - PRNewswire nevin manimala

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Operation Night Hawk Statistics – PAHomePage.com

Operation Night Hawk Statistics - PAHomePage.com nevin manimala

LUZERNE COUNTY – (WBRE/WYOU) Pennsylvania State Police have released stats regarding their two day “Operation Night Hawk” initiative in Luzerne County.
More than 100 state and municipal officers participated in the unique DUI enforcement initiative on Friday and Saturday.

Arrest statistics are as follows:

DUI Arrests: 121

Misdemeanor Arrests: 32

Felony Arrests: 5

Driving Under Suspension Citations: 46

Driving Under Suspension DUI Related: 12

Seatbelt Citations: 16

Child Seat Citations: 4

Speeding Citations: 11

Other Traffic Citations: 330

Underage Drinking Arrests: 3

Misdemeanor / Felony Wanted Individuals Arrested: 15

Motorists Contacted: 1115

During the course of this proactive DUI enforcement effort, a PSP Sergeant observed a burglary in progress and took the actors into custody in Lackawanna County. Also in Lackawanna County, a Trooper apprehended a convicted felon who was reported to have fired shots in a crowded area. The firearm was also recovered. In Luzerne County, Troopers recovered a vehicle that had been reported stolen from Centre County and arrested the driver. And a significant drug seizure was also made during a traffic stop initiated by a PSP Corporal.


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North-South football: Game statistics, past results | Football – Charleston Gazette-Mail

North-South football: Game statistics, past results | Football - Charleston Gazette-Mail nevin manimala
North-South football: Game statistics, past results | Football - Charleston Gazette-Mail nevin manimala

At South Charleston High School

North;0;0;8;0;–;8

South;0;0;0;6;–;6

Third quarter

N — Heston 73 pass from Neal (Hollaway pass from Neal), 8:27

Fourth quarter

S — Hensley 7 pass from Hackney (pass failed), 1:02

Team statistics

;;North;South

First downs;;8;4

Rushes-yards;;24-26;22-48

Passing yards;;148;87

Passes;;7-25;2;9-15-2

Total yards;;174;135

Fumbles-lost;;0-0;1-1

Penalties-yards;;6-55;4-33

Punts-average;;2-27.0;5-33.8

INDIVIDUAL STATISTICS

RUSHING — North: Pollock 5-17, Beard 6-15, Heston 3-2, Davidson 3-minus-1, Neal 7-minus-7; South: Chafin 8-54, Clark 2-6, Lesher 1-4, Bartram 2-0, Gray 2-0, Hensley 1-minus-1, Adkins 1-minus-7, Hackney 5-minus-8

PASSING — North: Neal 6-19-2-1-3, Beard 1-5-0-45, McKinney 0-1-0-0; South: Hackney 9-15-2-87, Adkins 1-3-2-10

RECEIVING — North: Posey 2-21, Heston 1-73, Redman 1-45, Parchment 1-7, Davidson 1-1, Hollaway 1-1; South: Hensley 6-59, Wamsley 2-27, Wynn 1-1

KICKOFF RETURNS — North: Hollaway 1-24, Puffenberger 1-0; South: Gray 1-27, Hall 1-1

PUNT RETURNS — North: Posey 1-21; South; Hensley 1-15

INTERCEPTION RETURNS — North: Hartman 1-13, Bish 1-0; South: Hensley 2-0

FUMBLE RETURNS — North: Metzgar 1-0; South: none

QB SACKS — North: Lang 1-9, Heston 1-7, Hartman 1-1; South: Battaile 1-10, Richmond 1-10

North-South football series

1934 — South 0, North 0

1935 — South 12, North 0

1936 — South 0, North 0

1937 — South 7, North 0

1938 — South 25, North 0

1939 — South 9, North 0

1940 — South 19, North 0

1941 — South 20, North 0

1942 — North 25, South 7

1943 — South 25, North 20

1944 — South 9, North 7

1945 — South 3, North 0

1946 — South 7, North 7

1947 — South 20, North 13

1948 — South 25, North 7

1949 — North 14, South 9

1950 — North 12, South 9

1951 — North 27, South 0

1952 — South 19, North 18

1953 — South 14, North 13

1954 — North 26, South 18

1955 — South 40, North 2

1976 — North 8, South 6

1977 — South 23, North 6

1978 — South 14, North 9

1979 — South 15, North 8

1980 — North 23, South 0

1981 — South 7, North 3

1982 — North 29, South 12

1983 — North 19, South 14

1984 — South 14, North 13 (OT)

1985 — South 34, North 18

1986 — South 39, North 0

1987 — South 10, North 7

1988 — South 35, North 24

1989 — South 21, North 12

1990 — South 20, North 14

1991 — North 17, South 14

1992 — South 30, North 16

1993 — North 24, South 20

1994 — South 28, North 0

1995 — South 24, North 0

1996 — South 19, North 2

1997 — North 14, South 13

1998 — South 26, North 0

1999 — North 19, South 8

2000 — North 20, South 18

2001 — North 41, South 31

2002 — North 39, South 12

2003 — South 25, North 20

2004 — North 15, South 14

2005 — North 9, South 7

2006 — South 33, North 22

2007 — South 7, North 3

2008 — South 24, North 14

2009 — North 22, South 0

2010 — South 21, North 6

2011 — South 14, North 12

2012 — South 36, North 28 (OT)

2013 — South 43, North 14

2014 — South 46, North 26

2015 — South 7, North 0

2016 — North 42, South 35

2017 — North 10, South 7

2018 — North 42, South 20

2019 — North 8, South 6

NOTE: South leads all-time series 40-23-3

Most outstanding players

North-South most outstanding player award winners:

1985 –Hugh Smalls, Blufield (South); note: only one player selected

1986 — Richard Ceglie, Brooke (North); Bill Smith, George Washington (South)

1987 — Chris Lambert, North Marion (North); J.J. Abbott, Chapmanville (South)

1988 — Bryan Litton, Parkersburg South (North); Tom Zban, Huntington East (South)

1989 — Stephen Redd, Philip Barbour (North); Jon Jones, Stonewall Jackson (South)

1990 — Steve Malnick, North Marion (North); Jerome Ingram, Mount View (South)

1991 — Steven Rosi, Fairmont Senior (North); Robert Walker, Huntington (South)

1992 — Matt McCulty, Spencer (North); Mark Terry, South Charleston (South)

1993 — Ken Fisher, Magnolia (North); David Edwards, Mount View (South)

1994 — Michael Joseph, Fairmont Senior (North); Jerrald Long, Mount View (South)

1995 — Preston Thompkins, Parkersburg South (North); Norm Branch, Huntington (South)

1996 — Ryan Ward, Ravenswood (North); John Borosky, Mount View (South)

1997 — Chip Donohoe, Ravenswood (North); Monty Wright, Oak Hill (South)

1998 — Zach Anglin, Bridgeport (North); Ben Poe, Cabell Midland (South)

1999 — Brent Metheny, Moorefield (North); Chris Martin, Nitro (South)

2000 — Ike Weaver, Parkersburg (North); J.R. Harper, Clay County (South)

2001 — Pat Morrison, Lewis County (North); John Grow, Capital (South)

2002 — Marc Kimes, Parkersburg (North); Calvin Joplin, Matewan (South)

2003 — Jeff McCoy, Ripley (North); Derek Midkiff, Nitro (South)

2004 — Ben Gum, Parkersburg South (North); John Allevato, George Washington (South)

2005 — Jamin McCue, Bridgeport (North); Gordie Newsome, Huntington (South)

2006 — Parker Deem, Ripley (North); Nico Newell, Wayne (South)

2007 — Derek Sevier, East Fairmont (North); Steven Bumpus, Capital (South)

2008 — Jordan Griffin, Robert C. Byrd (North); Marquel Ali, Woodrow Wilson (South)

2009 — Cameron Gallaher, Grafton (North); James Woods, Capital (South)

2010 — Michael Molinari, Parkersburg South (North); Jake Lilly, Bluefield (South)

2011 — Connor Arlia, Madonna (North); Duran Workman, George Washington (South)

2012 — Brandon Ashenfelter, Martinsburg (North); Cody Carter, Cabell Midland (South)

2013 — Justin Cookie Clinton, Martinsburg (North); Andrew Johnson, Woodrow Wilson (South)

2014 — Zach Phillips, Wheeling Park (North); Kane Roush, Wahama (South)

2015 — Max Chefren, Parkersburg (North); Noah Hancock, Woodrow Wilson (South)

2016 — Juwan Jones-Wright, Robert C. Byrd (North); Druw Bowen, George Washington (South)

2017 — Isaiah Utt, University (North); Darnell Brooks, George Washington (South)

2018 — Brett Tharp, East Hardy (North); Curon Cordon, Hurricane (South)

2019 — Rhett Heston, Fairmont Senior (North); J.T. Hensley, Sherman (South)

New York City Police Department’s Hate Crime Racial Statistics Highlight Lie In De Blasio’s Narrative – The Daily Caller

New York City Police Department’s Hate Crime Racial Statistics Highlight Lie In De Blasio’s Narrative - The Daily Caller nevin manimala
9:20 PM 06/07/2019 | US

Scott Morefield | Reporter

New York City Mayor Bill de Blasio blamed “forces of white supremacy” for an attack on a group of Hasidic Jews in Crown Heights, Brooklyn, last month, even though the actual attackers were black.

“It’s really clear that forces of white supremacy have been unleashed,” the 2020 presidential candidate said. “A lot of folks used to be told it was unacceptable to be anti-Semitic, it was unacceptable to be racist and now they’re getting more permission.”

While it is true in some cases that hate crimes involve the narrative of the “angry white male” wreaking havoc on marginalized victims, the actual statistics of New York’s police department appear to tell another story.

In de Blasio’s own city, NYPD crime reports cite hate-crime victims as being “most frequently [w]hite” at 46.1% and hate crime suspects as “most frequently Black” at 55%.

New York City Police Department’s Hate Crime Racial Statistics Highlight Lie In De Blasio’s Narrative - The Daily Caller nevin manimala

NEW YORK, NY – OCTOBER 25: (L-R) NYPD Chief of Department Terence Monahan, New York City Mayor Bill de Blasio and New York City Police Commissioner James ONeill exit a press conference regarding the recent package bombings, at NYPD headquarters, October 25, 2018 in New York City. (Photo by Drew Angerer/Getty Images)

From the NYPD’s analysis:

Hate crime victims are most frequently White (46.1%) while Black (24.7%) and Hispanic (20.1%) hate crime victims account for a significant portion of all hate crime victims. Asian/Pacific Islander hate crime victims (9.1%) account for the remaining portions of hate crime victims.

Hate crime suspects are most frequently Black (55.0%), White (23.6%) or Hispanic (20.0%). Asian/Pacific Islanders
accounted for (1.4%) of the remaining suspects.

The hate crime arrest population is most frequently White (51.3%) or Black arrestees (44.7%). Asian/Pacific Islander arrestees (2.7%) and Hispanic (1.3%) account for the remaining portions of the hate crime arrest population.

Here are New York City’s current demographic estimates, according to World Population Review:

White: 42.78%
Black or African American: 24.32%
Other race: 15.12%
Asian: 14.00%
Two or more races: 3.33%
Native American: 0.40%
Native Hawaiian or Pacific Islander: 0.05%

In the first quarter of 2019, there were 49 total hate crime arrests: 25 arrestees were white (51% of hate crime arrests, 42.78% population), 17 were black (34.7% of hate crimes arrests, 24.32% of population), and five were Asian (10.2% of hate crime arrests, 14% of population).

Yearly 2018 statistics tell a similar tale. Of 151 total crimes resulting in arrest, 77 arrestees were white (51% of hate crime arrests, 42.78% population) and 67 were black (44.4% of hate crime arrests, 24.32% of the population). Only four of those arrested were of Asian ancestry. (RELATED: Reason Editor Explains How Hate Crime Statistics Are Misrepresented, Give Shocking Guess On How Many Are Actually Real)

It was unclear why only 23.6% of hate crime suspects were white while 51% of hate crime arrests were also white. Nevertheless, the breakdown of even white arrests for hate crimes as a percentage of the white population (51% vs 42.78%) is still lower than that for blacks (44.4% vs 24.32%).

The Daily Caller asked The Manhattan Institute’s Heather Mac Donald for some insight into why this could be:

If the NYPD characterizes domestic stalking and other types of non-violent domestic disputes as a hate crime based on gender — and I don’t know if this is the case — it would be easier to arrest suspects, since the suspect would be known. There may be more whites proportionally involved in non-violent domestic abuse than in stranger hate crimes, on the analogy with stranger street crime. A larger proportion of white committed homicides than black committed homicides are domestic violence, because whites are only negligibly involved in street crime. In New York City, for example, blacks are 72% of all shooting suspects, and whites less than 3%, though the black and white proportions of the city’s population are around 24% and 43%, respectively.

“But if non-violent domestic abuse were included in the hate crime category, you would expect to see a higher percentage of Hispanic suspects, since they have high rates of domestic violence,” she continued.

Mac Donald said she supposes “it’s possible” that police might decide not to characterize an assault as a hate crime after picking up a minority suspect but there’s “no evidence” to support such a claim: “I suppose that is possible, but I have no evidence for that. A portion of black-on-white street violence may have some element of contempt that rarely is acknowledged or charged. But that is a different matter. I wish I had an answer, but it is a very interesting discrepancy.”

The Daily Caller has reached out to NYPD for comment but did not receive an answer in time for publication.

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