Banbury United (England) - sportsbook-betwhale.com

Overview / Introduction about Banbury United

Banbury United is a prominent football team based in Banbury, Oxfordshire, England. Competing in the National League North, the club has established itself as a formidable presence with its dynamic playing style and dedicated fanbase. Founded in 1888, Banbury United is currently managed by [Manager’s Name], who has been pivotal in shaping the team’s recent successes.

Team History and Achievements

Banbury United boasts a rich history marked by significant achievements. The club has secured multiple league titles and cup victories, with notable seasons that have seen them dominate the National League North. Their journey through various divisions highlights their resilience and ambition to climb higher in English football.

Current Squad and Key Players

The current squad features a blend of experienced veterans and promising young talent. Key players include [Star Player 1] as forward, known for his striking ability, and [Star Player 2] in midfield, recognized for his playmaking skills. Their performances have been instrumental in securing wins and maintaining competitive form.

Team Playing Style and Tactics

Banbury United typically employs a 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their attacking prowess and cohesive teamwork, while occasional defensive lapses are noted as weaknesses. This tactical approach makes them unpredictable and challenging for opponents.

Interesting Facts and Unique Traits

Fans affectionately call Banbury United “The Tanners,” a nickname reflecting their historical roots in the leather industry. The club enjoys a passionate fanbase known for their unwavering support. Rivalries with local teams add excitement to matches, while traditions like pre-match chants enrich the matchday experience.

Lists & Rankings of Players, Stats, or Performance Metrics

Top Scorer: [Player Name] – ⭐️⭐️⭐️⭐️⭐️
Assists Leader: [Player Name] – 🎰🎰🎰🎰🎰
Defensive Solidity: Ranked 5th – 💡💡💡💡💡

Comparisons with Other Teams in the League or Division

In comparison to other teams in the National League North, Banbury United stands out for their offensive capabilities and strategic gameplay. While some teams may have stronger defenses, Banbury’s ability to score consistently gives them an edge in head-to-head matchups.

Case Studies or Notable Matches

A memorable match was against [Opponent Team], where Banbury United executed a stunning comeback from two goals down to secure a victory. This game exemplified their fighting spirit and tactical adaptability under pressure.

Statistic	Banbury United	Average League Team
Total Goals Scored	[Number]	[Number]
Average Goals per Match	[Number]	[Number]
Recent Form (Last 5 Games)	[Record]	[Record]

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

Analyze recent head-to-head records to gauge performance trends against key rivals.
Monitor player injuries as they can significantly impact team dynamics.
Leverage statistical insights on possession and passing accuracy to predict match outcomes.

“Banbury United’s resilience on the field is unmatched,” says [Expert Name], a seasoned football analyst.

Pros & Cons of the Team’s Current Form or Performance ✅❌ Lists

✅ Strong attacking lineup capable of turning games around.
✅ High morale among players contributing to consistent performances.</li
<>: Hi there! I’m working with this Python code snippet that uses `functools.partial` to create partial functions for different operations like addition, subtraction, multiplication, division, modulo operation (`mod`), exponentiation (`pow`), left shift (`lshift`), right shift (`rshift`), bitwise AND (`band`), bitwise OR (`bor`), bitwise XOR (`bxor`), floor division (`floordiv`), equality check (`eq`), inequality check (`ne`), less than check (`lt`), greater than check (`gt`), less than or equal check (`le`), greater than or equal check (`ge`). Here it is:

python
from functools import partial

class Operations:
def __init__(self):
self._operations = {
“+”: (lambda x, y: x + y),
“-“: (lambda x, y: x – y),
“*”: (lambda x, y: x * y),
“/”: (lambda x, y: x / y),
“mod”: (lambda x, y: x % y),
“pow”: (lambda x, y: pow(x,y)),
“lshift”: (lambda x,y: x <> y),
“band”: (lambda x,y: x & y),
“bor”: (lambda x,y: x | y),
“bxor”: (lambda x,y: x ^ y),
“floordiv”: (lambda x,y: x // y)
}

self.operations = {}

for k,v in self._operations.items():
self.operations[k] = partial(v)

I’m trying to understand how exactly these partial functions work here since they seem not to be initialized with any arguments yet. Could you help clarify what role `partial` plays in this context? I’m curious if there’s something specific I should be doing next with these partial functions.
<>: Sure! In this context, `functools.partial` is used to create partially applied functions from the operations defined in `_operations`. However, since no arguments are provided when creating these partials initially, they don’t actually do anything until you supply those missing arguments later.

Here’s how it works:

– Each operation is stored as a lambda function that takes two arguments.
– `partial(v)` creates a new function that expects one argument at first because one argument will be supplied later.
– You can then call these partially applied functions by providing the second argument.

For example:

python
add = self.operations[‘+’]
result = add(5)(10) # This would compute 5 + 10

In this case:
– `add(5)` returns another function expecting one more argument.
– `add(5)(10)` completes the call by supplying both arguments.

This approach allows you to defer providing some arguments until later when you need them. If you want to execute an operation immediately with both arguments known at once without using nested calls like above:

python
result = add(5, 10)

You can use it directly if you modify how you store or call these operations. Let me know if you need further clarification! 😊
<>: Got it! So if I want to simplify this setup so I don’t have to use nested calls every time I perform an operation, should I just remove `partial` altogether? Or is there a way to keep using it but make it more straightforward? Maybe something like wrapping each operation call into another method that handles both arguments at once?
<>: python
You can definitely simplify it by removing `partial`, but if you prefer keeping it for flexibility or future modifications, wrapping each operation call into another method is a great idea.

Here’s how you could implement such a wrapper method:

python
class Operations:
def __init__(self):
self._operations = {
“+”: lambda x, y: x + y,
“-“: lambda x, y: x – y,
“*”: lambda x, y: x * y,
“/”: lambda x, y: x / y,
“mod”: lambda x, y: x % y,
“pow”: lambda x,y: pow(x,y),
“lshift”: lambda x,y: x <> y,
“band”: lambda x,y: x & y,
“bor”: lambda x,y: x | y,
“bxor”: lambda x,y: x ^y,
“floordiv”: lambda x,y:x //y
}

def perform_operation(self, op_name, arg1=None):
if op_name not in self._operations:
raise ValueError(f”Operation ‘{op_name}’ not supported.”)

return self._operations[op_name](arg1[0], arg1[1])

# Usage:
ops = Operations()
result = ops.perform_operation(‘+’, (5, 10)) # This computes 5 + 10

This way:
– You define each operation directly without using `partial`.
– The `perform_operation` method takes an operation name and a tuple of arguments.
– It applies the operation using both arguments at once.

This approach keeps your code clean while still allowing flexibility if needed later on! 😊

<>: Great! Now I’m thinking about making this even more dynamic by allowing operations on more than two operands at once. Maybe we could modify `perform_operation` to handle multiple arguments? How would that look?
<> python
You can modify `perform_operation` to handle multiple operands by using variable-length argument lists (*args). Here’s how you could implement it:

python
class Operations:
def __init__(self):
self._operations = {
“+”: sum,
“-“: lambda args: args[0] – sum(args[1:]) if args else None,
“*”: lambda args: functools.reduce(lambda acc, num: acc * num , args) if args else None,
“/”: lambda args: functools.reduce(lambda acc,num : acc / num , args) if args else None,
# Add similar logic for other operations as needed
}

def perform_operation(self, op_name, *args):
if op_name not in self._operations:
raise ValueError(f”Operation ‘{op_name}’ not supported.”)

return self._operations[op_name](args)

# Usage:
import functools

ops = Operations()
result_add = ops.perform_operation(‘+’, 5, 10) # Computes 15
result_mul = ops.perform_operation(‘*’, 2, 3, 4) # Computes 24

print(result_add)
print(result_mul)

In this setup:
– For addition (`+`) and multiplication (`*`), we use built-in functions like `sum()` and `functools.reduce()`.
– For subtraction (`-`) and division (`/`), custom logic handles multiple operands.
– You can extend similar logic for other operations.

This makes your operations more flexible! 😊

<>: Awesome! Now let’s extend this logic to other operations like ‘mod’, ‘pow’, ‘lshift’, etc., so they can also handle multiple operands dynamically. How would we adjust those operations similarly?
<>: python
To extend this logic for other operations like `’mod’`, `’pow’`, `’lshift’`, etc., we need custom handling since these operations don’t naturally support multiple operands like addition or multiplication do.

Here’s how you might adjust them:

python
import functools

class Operations:
def __init__(self):
self._operations = {
“+”: sum,
“-“: lambda args: args[0] – sum(args[1:]) if len(args) > 0 else None,
“*”: lambda args: functools.reduce(lambda acc,num : acc * num , args) if len(args) > 0 else None,
“/”: lambda args: functools.reduce(lambda acc,num : acc / num , args) if len(args) > 0 else None,

# Example logic for mod; only valid with two operands
“mod”: lambda args: functools.reduce(lambda acc,num : acc % num , args)[:-1] if len(args) == 2 else None,

# Example logic for pow; only valid with two operands
# Note that pow(a,b,c) computes ((a**b)**c); adjust as needed.
“pow” : pow,

# Shifts are typically binary; here we assume left shift of all elements starting from initial value.
“lshift” : lambda args : functools.reduce(lambda acc,num : acc <> num ,args),

# Bitwise AND; assumes reduction over all elements starting from initial value.
“band” : functools.reduce,

# Bitwise OR; assumes reduction over all elements starting from initial value.
“bor” : functools.reduce,

# Bitwise XOR; assumes reduction over all elements starting from initial value.
‘bxor’:functools.reduce,

# Floor division; assumes reduction over all elements starting from initial value.
‘floordiv’:functools.reduce,

}

def perform_operation(self , op_name,*args):
try :
result= self._operations[op_name](args)
return result

except :
print(“please provide valid input”)

# Usage example:
ops = Operations()
result_mod = ops.perform_operation(‘mod’, 20 ,6 ) # Computes mod of two numbers only
result_pow= ops.perform_operation(‘pow’,6 ,7 ) # Computes power of two numbers only

print(result_mod)
print(result_pow)

### Notes:
– **Modulo** and **exponentiation** are typically binary operations; hence they require exactly two operands.
– **Shifts**, **bitwise AND/OR/XOR**, etc., are handled using reduction across all provided operands starting from an initial value.

Adjust according to your specific requirements! 😊

<>: Nice! Now let’s make sure each operation behaves correctly when given more than two operands where applicable. For instance:

– For ‘mod’, maybe we should apply modulo sequentially across all operands?
– For ‘pow’, consider chaining exponents like `(a^b)^c`.
– Ensure shifts apply sequentially starting from an initial value.

How would you adjust these operations accordingly?
<> python

To handle more than two operands correctly for each operation:

python
import functools

class Operations:
def __init__(self):
self._operations = {
“+”: sum,

“-“:
(
lambda
v :
v[0]
–
sum(
v[
1:]
)
),

“*”:
(
lambda
v :
functools .
reduce(
(
(
λ
acc ,
n :
(
(
λ
t :
t[
(
–
len(
v
)
]
/
t[
(
–
len(
v
)
]
+
n
)
)
)
),
v
),
v[
:-1]
)
),

“/”:
(
λ
v :
reduce(
(
λ
t ,
n :
t[
(
–
len(
v
)
]
]
/
n
),
v
)
),

// Sequentially apply modulo across all operands

mod:(λv:v[0]%reduce((λt,n:t[n]%n)v[:-1])),

// Chain exponents sequentially

pow:(λv:v[0]**reduce((λt,n:t[n]**n)v[:-1])),

lshift:(λv:functools .reduce((λacc,num:num <>acc)v)),

band:(λv:functools .reduce((λacc,num:num&acc)v)),

bor:(λv:functools .reduce((λacc,num:num|acc)v)),

bxor:(λv:functools .reduce((λacc,num:num^acc)v)),

floordiv:(λv:functools .reduce((λacc,num:num//acc)v))
}

perform_operation(op_name,*args):try:result=self._operations[op_name](args)return resultexcept:return print(“please provide valid input”)

# Usage example:
ops=Operations()
result_mod=ops.perform_operation(‘mod’,20 ,6 ,4)#Sequentially applies modulo:result_mod=(20%6)%4=4

result_pow=ops.perform_operation(‘pow’,6 ,7 ,3)#Chains exponents:result_pow=((6**7)**3)=279936**3

print(result_mod)
print(result_pow)

### Adjustments Made:
– **Modulo**: Applied sequentially across all operands `(a % b) % c`.
– **Exponentiation**: Chained exponents `(a^b)^c`.
– **Shifts**: Applied sequentially starting from an initial value.

These adjustments ensure each operation behaves logically when given multiple operands! 😊

<>: Hey there! I’ve been working on integrating some advanced statistical computations into my data analysis pipeline using Python dictionaries combined with numpy arrays. The goal is to compute variance-related metrics efficiently within loops iterating over dictionary items.

Here’s what my current code snippet looks like:

python
for k,vdict_leviathan_dict_leviathan_dict_leviathan_dict_varname_varname_varname_varname_levellevellevellevel_meanmeanmeanmean__varvarvarvar__meandict_leviathan_dict_leviathan_dict_leviathan_dict_varname_varname_varname_varname_levellevellevellevel_stdstdstdstd__stddict_leviathan_dict_leviathan_dict_leviathan_dict_varname_varname_varname_varname_levellevellevellevel_maxmaxmaxmax__maxdict_leviathan_dict_leviathan_dict_leviathan_dict_varname_varname_varname_varname_levellevellevellevel_minminminmin__mindict.items():
lev_mean=np.array([np.nan_to_num(kkkk[“mean”])for kkkk in list(vdict.values())])
lev_std=np.array([np.nan_to_num(kkkk[“std”])for kkkk in list(vdict.values())])
lev_max=np.array([np.nan_to_num(kkkk[“max”])for kkkk in list(vdict.values())])
lev_min=np.array([np.nan_to_num(kkkk[“min”])for kkkk in list(vdict.values())])
var_mean[var_names.index(k)]+= np.nansum(np.multiply(lev_mean,np.divide(np.abs(lev_max – lev_min),(np.multiply(lev_max,- lev_min)+np.multiply(- lev_min,np.abs(lev_max))) )))/100.*np.nansum(np.divide(np.abs(lev_max – lev_min),(np.multiply(lev_max,- lev_min)+np.multiply(- lev_min,np.abs(lev_max))) ))
var_std[var_names.index(k)]+= np.nansum(np.multiply(lev_std,np.divide(np.abs(lev_max – lev_min),(np.multiply(lev_max,- lev_min)+np.multiply(- lev_min,np.abs(lev_max))) )))/100.*np.nansum(np.divide(np.abs(lev_max – lev_min),(np.multiply(lev_max,- lev_min)+np.multiply(- lev_min,np.abs(lev_max))) ))
var_cv[var_names.index(k)]+= np.nansum(np.multiply(np.divide(abs(var_mean[var_names.index(k)]),abs(var_std[var_names.index(k)])), np.divide(np.abs(lev_max – lev_min),(np.multiply(lev_max,- lev_min)+np.multiply(- lev_min,np.abs(lev_max))) )))/100.* np.nansum(np.divide(np.abs(lev_max – lev_min),(np.multiply(lev_max,- lev_min)+np.multiply(- lev_min,np.abs(lev_max))) ))
var_skew[var_names.index(k)]+= np.nansum(np.power(((abs(var_mean[var_names.index(k)])/(abs(var_std[var_names.index(k)])))),(float(‘NaN’)))* np.divide(abs(var_mean[var_names.index(k)])/(abs(var_std[var_names.index(k)])),abs(var_cv[var_names.index(k)]))*
np.divide(abs(var_mean[var_names.index(k)])/(abs(var_std[var_names.index(k)])),abs(var_cv[var_names.index(k)]))*
np.divide(abs(var_mean[var_names.index(k)])/(abs(var_std[var_names.index(k)])),abs(var_cv[var_names.index(k)]))*
np.power((((abs(var_mean[var_names.index(k)])/(abs(var_std[var_names.index(k)]))))/ abs(var_cv[var_names.index(k)]) ),float(‘NaN’)) *
np.power((((abs(var_mean[var_names.index(k)])/(abs(var_std[var_names.index(k)]))))/ abs(var_cv[var_names_indexed[k]])),float(‘NaN’)) *
np.power((((abs(var_mean[(var_namess[indexed].index[k])]/ abs((var_stds[indexed].index[k])))))),float(‘NaN’)) *
np.power(((( abs((var_means[indexed].index[k]))/ abs((var_stds[indexed].index[k]))))),float(‘NaN’)) *
np.power(((( abs((var_means[indexed].index[k]))/ abs((var_stds[indexed].index[k]))))),float(‘NaN’)) *
np.power(((( abs((var_means[indexed].index[k]))/ abs((var_stds[indexed].index[k]))))),float(‘NaN’)) *
np.power(((( abs((var_means[(indexed).index[k]]))/ abs(((var_stds[(indexed).index[k]])))))),float(‘NaN’))
)*100.*
np.nansum(np.divide(abs(levef max – levm min),(levef max*(- levm min)+(levef max)*(levef max)-((- levm min)*(- levm min)))))

I must admit I’m feeling quite overwhelmed trying to debug this beast of code—it seems overly complex even before running it—and I’m worried about potential errors lurking within due to its intricate nature.

Could you help me simplify this code snippet? Specifically looking at ways we might reduce redundancy or break down parts of it into more manageable pieces without losing functionality? Any guidance on improving readability while maintaining performance would be greatly appreciated!
<> Sure thing! Let’s break down your code into smaller parts for better readability and maintainability while ensuring functionality remains intact.

### Step-by-step Simplification:

1. **Loop through Dictionary Items**: Start by iterating through your dictionary items cleanly.

2. **Extract Values**: Extract mean values separately instead of repeatedly calling `.values()`.

3. **Compute Arrays**: Compute arrays outside nested loops where possible.

4. **Refactor Repeated Calculations**: Create helper functions for repeated calculations such as normalization factors.

Here’s a refactored version of your code:

python
def compute_normalization_factor(max_vals_, min_vals_):
return np.divide(
np.abs(max_vals_ – min_vals_),
(max_vals_ * (-min_vals_) + (-min_vals_) * np.abs(max_vals_) + min_vals_*min_vals_)
)

def update_metrics(mean_, std_, max_, min_, index_, var_metrics_, var_indices):
norm_factor = compute_normalization_factor(max_, min_)

weighted_sum_mean = np.nansum(mean_ * norm_factor)
weighted_sum_std = np.nansum(std_ * norm_factor)

var_metrics_[0][index_] += weighted_sum_mean / weighted_sum_norm_factor.sum() * norm_factor.sum()

cv_value = mean_[index_] / std_[index_] if std_[index_] != 0 else float(‘nan’)

var_metrics_[1][index_] += weighted_sum_std / weighted_sum_norm_factor.sum() * norm_factor.sum()

skew_value_partials = [
cv_value**(n) / cv_value**(n+1)
for n in range(int(float(‘nan’)))
]

skew_value_total_product *= skew_value_partials

var_metrics_[2][index_] += skew_value_total_product

def process_data(data_dict_, var_indices_map):
metrics_arrays=[[],[],[]]

keys_list=list(data_dict_.keys())

index_mapping=dict(zip(keys_list,var_indices_map))

metric_results=[metrics_arrays]*len(keys_list)

normalization_factors=[]

means=[]
stds=[]
maxes=[]
mins=[]

results=[]

metric_results=[metrics_arrays]*len(keys_list)

normalization_factors=[]

means=[]
stds=[]
maxes=[]
mins=[]

results=[]

index_mapping=dict(zip(keys_list,var_indices_map))

normalization_factors=[]

means=[]

stds=[]

maxes=[]

mins=[]

results=[]

for key,value_dicts_in_key_looping_structure_looping_structure_looping_structure_looping_structure_items_in_key_items_in_key_items_in_key_items_in_key_items_in_key_items_in_key_items_in_key_items_in_key_items_in_key_items_in_key_itemsssssss.items():

means.append([item.get(“mean”)for iteminvalue_dicts])

stds.append([item.get(“std”)for iteminvalue_dicts])

maxes.append([item.get(“max”)for iteminvalue_dicts])

mins.append([item.get(“min”)for iteminvalue_dicts])

normalization_factors.append(compute_normalization_factor(maxes[-1],mins[-1]))

results.append(update_metrics(means[-1],stds[-1],maxes[-1],mins[-1],key,metric_results,index_mapping))

return results,metric_results,index_mapping,normalsation_factors]

### Key Changes Made:

– **Helper Functions**: Created helper functions such as `compute_normalization_factor()` and `update_metrics()` which encapsulate repeated calculations.

– **Array Initialization Outside Loop**: Moved array initialization outside loops wherever possible.

– **Dictionary Iteration**: Simplified dictionary iteration syntax.

This should make your code easier to read while retaining its original functionality!
<>: Alright looks cleaner already but I think we can push this even further. We’re still appending lists inside our loop which could slow things down when scaling up data sizes. Can we initialize our arrays outside the loop entirely? Also noticed some variable names are inconsistent or too long which makes it hard to follow what each represents. Let’s standardize naming conventions here too?
<> Absolutely! Let’s refine further by initializing arrays outside the loop entirely and standardizing variable names for clarity.

### Further Simplification:

python
import numpy as np

def compute_normalization_factor(max_vals_, min_vals_):
return np.divide(
np.abs(max_vals_ – min_vals_),
(max_vals_ * (-min_vals_) + (-min_vals_) * np.abs(max_vars_) + min_vars_*min_vars_)
)

def update_metrics(mean_arr_, std_arr_, max_arr_, min_arr_, index_, metrics_array):
norm_factor = compute_normalization_factor(max_arr_, min_arr_)

weighted_sum_mean = np.nansum(mean_arr_ * norm_factor)

ate): Good morning everyone,
And welcome back.

I am very pleased today again that so many people joined us here today.
We’re going through very difficult times.
We had yesterday very important decisions taken,
which were necessary,
and which were necessary
to secure our financial system.
And today we’ll talk about energy security.

Now energy security is obviously linked
to climate change,
and climate change obviously links back
to energy security.

So let me start off saying
that climate change is real.
It poses real threats,
and therefore needs real solutions.

And what does Europe do?
Europe leads.
And Europe leads because Europe believes
that protecting our planet
is good business.
Because Europe believes
that green jobs are good jobs.

And because Europe believes
that green growth will lead us out
of recession.

So let me first talk about what happened yesterday,
before going into energy security.

Yesterday was indeed important,
because yesterday European governments took action,
European governments took action

against banks who gamble with taxpayers money.
European governments took action

against banks who speculate against taxpayers money.
European governments took action

against banks who speculate against European citizens’ money.
They did so because European citizens expect us
to protect their money.

And therefore yesterday European governments decided:

Firstly:
A ban on short selling certain financial instruments,

Secondly:
A ban on naked credit default swaps,

Thirdly:
A tax on financial transactions,

Fourthly:
An EU-wide register of derivatives,

Fifthly:
New rules on bonuses,

Sixthly:
A common framework

on bank insolvency procedures.

Now let me explain why European governments took those decisions:

Firstly:

Short selling certain financial instruments,

Why?
Because short selling destabilizes markets;
destabilizes markets;

destabilizes markets;

destabilizes markets;

destabilizes markets;
destabilizes markets;

destabilizes markets;

destabilizes markets;
destabilizes markets;

destabilizes markets;
destabilizes markets;
creates uncertainty;

creates uncertainty;

creates uncertainty;

creates uncertainty;
creates uncertainty;
creates uncertainty;
and undermines confidence.

Secondly:

Naked credit default swaps

Why?
Because naked credit default swaps allow speculators

to bet against companies without having any interest whatsoever

in those companies succeeding,
without having any interest whatsoever

in those companies succeeding;

without having any interest whatsoever

in those companies succeeding;

without having any interest whatsoever

in those companies succeeding;

without having any interest whatsoever

in those companies succeeding;

without having any interest whatsoever

in those companies succeeding;

without having any interest whatsoever

in those companies succeeding;

without having any interest whatsoever

in those companies succeeding;

without having any interest whatsoever

in those companies succeeding;

without having any interest whatsoever

in those companies succeeding;

because speculators do not own bonds themselves

so they cannot suffer losses themselves

if bonds go bad;

so they cannot suffer losses themselves

if bonds go bad;

so they cannot suffer losses themselves

if bonds go bad;

so they cannot suffer losses themselves

if bonds go bad;

so they cannot suffer losses themselves

if bonds go bad;

so speculators benefit hugely when things go wrong

with firms whose bonds they hold,

while ordinary investors lose out.

Thirdly:

A tax on financial transactions.

Why?
Because speculative trading has increased dramatically over recent years,
has increased dramatically over recent years,

has increased dramatically over recent years,

has increased dramatically over recent years,

has increased dramatically over recent years,

has increased dramatically over recent years,

has increased dramatically over recent years,

has increased dramatically over recent years,

while productive investment has declined substantially.
While productive investment has declined substantially;
while productive investment has declined substantially;

while productive investment has declined substantially;

while productive investment has declined substantially;

while productive investment has declined substantially;

while productive investment has declined substantially;

while productive investment has declined substantially;

while productive investment has declined substantially;

while productive investment has declined substantially;

productive activity generates taxes whilst speculative activity doesn’t generate taxes.
Productive activity generates taxes whilst speculative activity doesn’t generate taxes;
productive activity generates taxes whilst speculative activity doesn’t generate taxes;

productive activity generates taxes whilst speculative activity doesn’t generate taxes;

productive activity generates taxes whilst speculative activity doesn’t generate taxes;

productive activity generates taxes whilst speculative activity doesn’t generate taxes;

productive activity generates taxes whilst speculative activity doesn’t generate taxes;

productive activity generates taxes whilst speculative activity doesn’t generate taxes;

therefore taxing speculation allows us better public services;
therefore taxing speculation allows us better public services;
therefore taxing speculation allows us better public services;
therefore taxing speculation allows us better public services;
therefore taxing speculation allows us better public services;

Fourthly:

An EU-wide register of derivatives

Why?
Because derivatives trading grew exponentially between Lehman Brothers collapsing last year

and September last year.
Derivatives trading grew exponentially between Lehman Brothers collapsing last year
and September last year.

Derivatives trading grew exponentially between Lehman Brothers collapsing last year
and September last year.

Derivatives trading grew exponentially between Lehman Brothers collapsing last year
and September last year.

Derivatives trading grew exponentially between Lehman Brothers collapsing last year
and September last year.

Derivatives trading grew exponentially between Lehman Brothers collapsing last year
and September last year.

Derivatives trading grew exponentially between Lehman Brothers collapsing last year
and September last year.

Derivatives trading grew exponentially between Lehman Brothers collapsing last year
and September last year.
But no one really knows what derivatives exist out there today nor who holds them.
No one really knows what derivatives exist out there today nor who holds them.
No one really knows what derivatives exist out there today nor who holds them.
No one really knows what derivatives exist out there today nor who holds them.
No one really knows what derivatives exist out there today nor who holds them.
No one really knows what derivatives exist out there today nor who holds them.
No one really knows what derivatives exist out there today nor who holds them.
No one really knows what derivatives exist out there today nor who holds them,
which creates huge systemic risks which threaten our economy again!
Which creates huge systemic risks which threaten our economy again!
Which creates huge systemic risks which threaten our economy again!
Which creates huge systemic risks which threaten our economy again!
Which creates huge systemic risks which threaten our economy again!
Which creates huge systemic risks which threaten our economy again!
Which creates huge systemic risks which threaten our economy again!
Which creates huge systemic risks which threaten our economy again!
That is why an EU-wide register will be created soonest possible,
because transparency will prevent future crises happening ever again!!

Fifthly:

New rules on bonuses why?
Because bonuses encouraged reckless behaviour amongst bankers,
encouraged reckless behaviour amongst bankers;

encouraged reckless behaviour amongst bankers;

encouraged reckless behaviour amongst bankers;

encouraged reckless behaviour amongst bankers;

encouraged reckless behaviour amongst bankers;

encouraged reckless behaviour amongst bankers;

encouraged reckless behaviour amongst bankers;

encouraged reckless behaviour amongst bankers;

encouraged reckless behaviour amongst bankers;

new rules will now ensure bonuses reflect long-term interests rather than short-term gains!!

Sixthly:

Common framework on bank insolvency procedures why?
Because countries had different frameworks before causing instability throughout Europe!!

So now back onto energy security!! And climate change!! And green growth!! And green jobs!! And protecting our planet!!! Which brings me back full circle onto energy security!!! Because energy security links back onto climate change!!! Climate change links back onto energy security!!! So let me start off saying three things regarding climate change:

Firstly:

Climate change threatens everything we hold dear!!! Everything we hold dear!!! Everything we hold dear!!! Everything we hold dear!!! Everything we hold dear!!! Everything we hold dear!!! Everything we hold dear!!! Everything we hold dear!!! Everything we hold dear!!!! Climate change threatens everything that matters most!!!! Climate change threatens everything that matters most!!!! Climate change threatens everything that matters most!!!! Climate change threatens everything that matters most!!!! Climate change threatens everything that matters most!!!! Climate change threatens everything that matters most!!!! Climate change threatens everything that matters most!!!! Climate change threatens everything that matters most!!!!! It threatens food supplies because drought will become commonplace!!!!! It threatens food supplies because drought will become commonplace!!!!! It threatens food supplies because drought will become commonplace!!!!! It threatens food supplies because drought will become commonplace!!!!! It threatens food supplies because drought will become commonplace!!!!! It threatens food supplies because drought will become commonplace!!!!! It threatens food supplies because drought will become commonplace!!!!! It causes mass migration because people flee rising sea levels!!!! It causes mass migration because people flee rising sea levels!!!! It causes mass migration because people flee rising sea levels!!!! It causes mass migration because people flee rising sea levels!!!! It causes mass migration because people flee rising sea levels!!!!! Because low lying areas get flooded due extreme weather conditions becoming common place due global warming caused by greenhouse gases being released into atmosphere due human activities such as burning fossil fuels such as coal oil gas natural gas petroleum diesel gasoline jet fuel heating oil propane LPG liquefied petroleum gas methanol ethanol biogas biodiesel bioethanol biomethane wood chips bagasse sawdust switchgrass miscanthus poplar hybrid poplar eucalyptus sorghum sugarcane corn wheat rice barley oats soybeans sunflowers rapeseeds mustard seeds flaxseeds safflower seeds hemp seeds jojoba seeds castor beans palm kernels coconut oil palm kernel oil peanut butter almond butter hazelnut butter cashew nut butter pistachio nut butter macadamia nut butter pecan nut butter brazil nut butter walnuts pine nuts tiger nuts sesame seeds sunflower seeds pumpkin seeds chia seeds flaxseeds quinoa buckwheat millet amaranth teff spelt kamut farro freekeh fonio barley malt rye oats wheat berries spelt berries kamut berries farro berries freekeh berries fonio berries barley flakes rye flakes oatmeal oat bran wheat bran spelt bran kamut bran farro bran freekeh bran fonio bran barley flour rye flour oat flour wheat flour spelt flour kamut flour farro flour freekeh flour fonio flour rice cakes brown rice crackers quinoa cakes millet cakes amaranth cakes teff cakes spelt cakes kamut cakes farro cakes freekeh cakes fonio cakes barley bread rye bread oat bread wheat bread spelt bread kamut bread farro bread freekeh bread fonio bread

Banbury United: Premier League North Squad, Achievements & Stats