Rudar U19: Premier League Squad, Stats & Achievements

Overview / Introduction about the Team

Rudar U19 is a prominent youth football team based in Montenegro, competing in the Montenegrin U19 League. The team is known for its strategic formation and development of young talent, with the current coach guiding them through competitive matches.

Team History and Achievements

Rudar U19 has a rich history, having been established in 2008. The team has consistently performed well in the league, securing several top positions. Notable achievements include winning the league title multiple times and reaching the finals in regional tournaments.

Current Squad and Key Players

The squad boasts several key players who have shown exceptional skills on the field. Among them are:

• Milan Vuković – Striker, known for his goal-scoring ability.
• Nikola Petrović – Midfielder, praised for his tactical awareness.
• Ivan Jovanović – Defender, recognized for his defensive prowess.

Team Playing Style and Tactics

Rudar U19 typically employs a 4-3-3 formation, focusing on quick transitions and maintaining possession. Their strengths lie in their disciplined defense and fast counter-attacks, while their weaknesses include occasional lapses in concentration during set pieces.

Interesting Facts and Unique Traits

The team is affectionately nicknamed “The Young Warriors” by their fans. They have a passionate fanbase that supports them fervently at every match. A notable rivalry exists with their local competitors, adding an extra layer of excitement to their games.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Milan Vuković – Top scorer with 15 goals this season.
• ❌ Nikola Petrović – Recently recovering from an injury but expected to return soon.
• 🎰 Ivan Jovanović – Consistently rated as one of the best defenders in the league.

Comparisons with Other Teams in the League or Division

Rudar U19 often compares favorably against other teams due to their strong youth development program. They frequently outperform teams with similar budgets by focusing on skillful play and tactical discipline.

Case Studies or Notable Matches

A breakthrough game for Rudar U19 was their victory against Sutjeska U19 last season, where they secured a win with a stunning last-minute goal that clinched the championship title for them.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Statistic	Data
Last Five Matches Result	W-W-D-L-W
Last Five Goals Scored/Away Goals Conceded Ratio (GF/GA)	10/5 (GF/GA)
Average Possession (%) Last Five Games	58%
Odds for Next Match Win/Loss/Tie (Hypothetical)	1.8/3.5/3.0 respectively

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

To make informed betting decisions on Rudar U19:

Analyze recent form trends to gauge momentum.Closely watch player fitness reports to predict lineup changes.Evaluate head-to-head records against upcoming opponents. Betting Tips:Bet on over/under goals based on recent scoring patterns.Leverage odds movements close to match time. Frequently Asked Questions (FAQs)What are Rudar U19’s strengths?Rudar U19 excels in defensive organization and quick counter-attacks due to their well-drilled midfielders and forwards.

How consistent is Rudar U19’s performance?The team has shown consistency over recent seasons by maintaining mid-table positions and occasionally challenging for titles when fully fit.

Who are key players to watch?Milan Vuković is crucial due to his scoring ability; Nikola Petrović’s playmaking skills are vital when he returns from injury; Ivan Jovanović provides stability at defense.

Potential Weaknesses?Sometimes lapses during set-pieces can be exploited by opponents; injuries affecting key players can also disrupt team cohesion temporarily.

Quotes or Expert Opinions about the Team (Quote Block)“Rudar U19 has consistently demonstrated resilience and strategic acumen under pressure,” notes a sports analyst covering Montenegrin youth leagues.”Pros & Cons of the Team’s Current Form or Performance (✅❌ Lists)

✅ Strong defensive setup leading to fewer goals conceded per match.

❌ Injuries affecting squad depth could impact upcoming fixtures.
✅ Effective youth development program producing talented prospects regularly.
self.min_node_elements:

x_min = self.x_range[:,0]

x_max = self.x_range[:,1]

y_min = self.y_range[:,0]

y_max = self.y_range[:,1]

# calculate centers

x_center = (x_min + x_max)/2

y_center = (y_min + y_max)/2

# create children nodes

child_1 = QuadTreeNode(

np.array([[x_min,x_center],[y_min,y_center]]),

np.array([[x_center,x_max],[y_center,y_max]]),

max_depth=self.max_depth,

min_node_elements=self.min_node_elements,

depth=self.depth+1

)

child_1.parent_node = self

child_1.elements += [e.copy() for e in list(filter(lambda e:e.in_box(child_1),self.elements))]

child_1._split()

child_4 = QuadTreeNode(

np.array([[x_min,x_center],[y_center,y_max]]),

np.array([[x_center,x_max],[y_max,y_min]]),

max_depth=self.max_depth,

min_node_elements=self.min_node_elements,

depth=self.depth+1

)

child_4.parent_node = self

child_4.elements += [e.copy() for e in list(filter(lambda e:e.in_box(child_4),self.elements))]

child_4._split()

if len(self.elements) > self.min_node_elements:

child_7 = QuadTreeNode(

np.array([[x_center,x_max],[y_center,y_max]]),

np.array([[x_max,x_min],[y_max,y_min]]),

max_depth=self.max_depth,

min_node_elements=self.min_node_elements,

depth=self.depth+1

)

child_7.parent_node = self

if len(self.elements) > self.min_node_elements:

child_8 = QuadTreeNode(

np.array([[x_center,x_max],[y_min,y_center]]),

np.array([[x_max,x_min],[y_center,y_max]]),

max_depth=self.max_depth,

min_node_elements=self.min_node_elements,

depth=self.depth+1

)

child_8.parent_node = self

if len(self.elements) > self.min_node_elements:

# find elements contained within each node.

# add those elements to corresponding node.

# split children.

#

#

#

#

#

#print(“split”)

elements_to_add_to_childs.append([e.copy() for e in list(filter(lambda e:e.in_box(child),self.elements))])

children.append(child)

children[-1]._split()

#

#

#

# check if maximum depth has been reached.

if not len(children)>0:

return

else:

# add elements contained within each node.

# split children.

children_to_split_children_of=[]

elements_to_add_to_childs=[]

else:

return

else:

return

else:

return

else:

return

else:

return

***** Tag Data *****
ID: 1
description: The _split method recursively divides a quadtree node into four smaller
nodes until certain conditions are met such as maximum depth or minimum number of
elements per node.
start line: 16
end line: 200
dependencies:
– type: Class
name: QuadTreeNode
start line: 2
end line: 200
context description: This method handles both spatial partitioning logic as well as
recursive subdivision of nodes which involves non-trivial operations such as element-wise
filtering based on spatial containment.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 5
interesting for students: ‘5’
self contained: N

*************
## Suggestions for complexity

1. **Dynamic Depth Adjustment Based on Element Density**: Modify `_split` so that it dynamically adjusts `max_depth` based on element density within each quadrant.

2. **Parallel Processing**: Implement parallel processing within `_split` using multithreading or multiprocessing to handle large datasets more efficiently.

3. **Custom Element Filtering Logic**: Allow custom filtering logic instead of just `in_box`. This could involve complex geometric shapes or user-defined criteria.

4. **Adaptive Node Splitting Criteria**: Change splitting criteria dynamically based on statistical analysis of element distribution rather than fixed thresholds like `min_node_elements`.

5. **Integration with Machine Learning Models**: Integrate machine learning models that predict whether further splitting would be beneficial based on historical data patterns.

## Conversation

<|user|>_Split code too long need optimize<|end|>

<|assistant|>_Sure! We can break down `_split` into smaller methods to improve readability and maintainability._

python
def _calculate_centers(self):
x_min, x_max = self.x_range[:,0], self.x_range[:,1]
y_min, y_max = self.y_range[:,0], self.y_range[:,1]
x_center, y_center = (x_min + x_max)/2, (y_min + y_max)/2

return x_min, x_max, y_min, y_max, x_center, y_center

def _create_child_nodes(self):
_, _, _, _, x_center, y_center = self._calculate_centers()

ranges_and_centers=[
([np.array([[self.x_range[:,0][i],(self.x_range[:,0][i]+self.x_range[:,1][i])/2],
[self.y_range[:,0][j],(self.y_range[:,0][j]+self.y_range[:,1][j])/2]])
,np.array([[(self.x_range[:,0][i]+self.x_range[:,1][i])/2,self.x_range[:,1][i]],
[(self.y_range[:,0][j]+self.y_range[:,1][j])/2,self.y_range[:,1][j]]])])
for i,j in [(0,0),(0,1),(1,0),(1 ,1)]]

return [QuadTreeNode(ranges_and_centers[i][0], ranges_and_centers[i][1],
max_depth=self.max_depth,min_node_elements=self.min_node_elements,
depth=self.depth+1)for i in range(4)]

def _filter_and_add_elements(self,node):
node.elements += [e.copy() for e in list(filter(lambda e:e.in_box(node),self.elements))]

Now we update `_split`:

python
def _split(self):
if len(self.elements) <= self.min_node_elements or (hasattr(self,'depth')and hasattr(self,'max_depth')and getattr(self,'depth')>=getattr(self,’max_depth’)):
return

children=[QuadTreeNode(*args,**kwargs)for args,kwargss
in zip((np.split(np.concatenate((np.tile(np.arange(len(range)),(len(range))),range)),range.size),)*4,
[np.tile(range,(len(range)))]*4,[{‘max_depth’:getattr(obj,’max’),’min_element’:getattr(obj,’min’),’depth’:getattr(obj,’depth’)+l}
for obj,l,_range_,*args_in_zip]for range,_range_,obj,*args_in_zip
in zip([np.split(np.concatenate((np.tile(np.arange(len(range)),(len(range))),range)),range.size)]*4,
[np.tile(range,(len(range)))]*4,[QuadTreeNode]*4,[{‘max’:getattr(obj,’max’),’min’:getattr(obj,’min’)}for obj
in [QuadTreeNode]*(len(args_in_zip))]))]

children=[child._filter_and_add_element(node)
for node,_child__list__in zip([QuadTreeNode]*len(children),children)]
children=[child._filter_and_add_element(node)
for node,_child__list__in zip([QuadTreeNode]*len(children),children)]

This makes it modular!<|end|>

<|user|>_Need dynamic adjust `max_depth` base density_<|end|>

<|assistant|>_We can modify `_calculate_density` method then adjust `max_depth` accordingly._

python
def _calculate_density(self):
area_x_y_ranges=np.prod(np.diff([*zip(*map(list,np.split(np.concatenate((np.tile(np.arange(len(range)),(len(range))),range)),range.size)))]))
num_elem=len(elements)

return num_elem / area_x_y_ranges

def _adjust_dynamic_depth_based_on_density(self):
density_threshold=10#example value adjust according your needs

if density >= density_threshold :
setattr(obj,”dynamic_adjustment”,True)

if hasattr(obj,”dynamic_adjustment”)and getattr(obj,”dynamic_adjustment”):
setattr(obj,”max_dynamic”,getattr(obj,”depth”)+int(density/density_threshold))
else :
setattr(obj,”max_dynamic”,getattr(obj,”max”))

def _split_with_dynamic_adjustment():
if hasattr(getattr(this_obj,”depth”))and hasattr(getattr(this_obj,”dynamic_adjustment”))and
getattr(this_obj,”dynamic_adjustment”)or
getattr(this_obj,”depth”)>=getattr(this_obj,”max_dynamic”):
return

#… continue original split logic …

This way we dynamically adjust `max_dynamic`. Adjust threshold value according your needs!<|end|>

***** Tag Data *****
ID: ‘5’
description: Multiple instances where recursive calls (_split()) are made conditionally.
start line: ’17’
end line: ‘199’
dependencies:
– type: Method/Functionality Name/Contextual Explanation/Other Dependencies Name?
start line:’16’
end line:’199′
context description:The repeated structure suggests that understanding this snippet’s”
algorithmic depth requires analyzing how recursion propagates through various conditions.”
algorithmic depth external:’N’
obscurity:’5′
advanced coding concepts:’5′
interesting for students:’5′
relevance:’N/A’
other notes:’N/A’

*************
## Suggestions for complexity:

Here are five advanced ways that the programmer might want to expand or modify logic specific to this code:

### Advanced Suggestions:

**Suggestion A:** Implementing Adaptive Quadrant Splitting Logic Based On Spatial Density
Instead of splitting quadrants uniformly regardless of element distribution density within each quadrant.

**Suggestion B:** Introducing Parallel Processing
Enhance performance by implementing parallel processing when recursively calling `_split()` function using Python’s multiprocessing library.

**Suggestion C:** Custom Element Filtering Logic
Allow users to define custom filtering functions beyond simple bounding box checks (`e.in_box`) via higher-order functions passed at initialization.

**Suggestion D:** Dynamic Depth Adjustment
Modify maximum recursion depth dynamically based on certain runtime metrics such as total number of elements processed so far.

**Suggestion E:** Enhanced Logging Mechanism
Implement an advanced logging mechanism that captures detailed information about each split operation including time taken per operation and memory usage statistics.

## Conversation:

<|user|>: I need help understanding how recursion works here especially why there are so many identical blocks? It seems redundant but maybe I’m missing something?<|end|>

<|assistant|>: The code you’ve provided indeed contains repetitive blocks which seem redundant at first glance. However these repetitions ensure that all four quadrants are processed individually after initial checks:

Each block creates one quadrant (`child_n`) from parent coordinates (`x`, `y`). It then assigns relevant elements from parent (`elements`) using a filter function (`lambda e:e.in_box(child_n)`). Finally it recursively calls `_split()` on each new quadrant ensuring further subdivisions until conditions halt further splits.

If you’re looking at optimization opportunities here redundancy could be reduced by refactoring repetitive tasks into reusable functions/methods while preserving logical flow:<| user>: Can you show me how I could refactor this? Maybe start by creating reusable functions?<| <|assistant|>: Certainly! Let’s start by refactoring repetitive tasks into separate helper methods within your class definition:

### Refactored Code Snippet:

python
class YourClassHere:
def __init__(…): # Your existing init code here…
…

# Helper method to create new QuadTreeNodes.
def create_child(x_bounds_low_high_tuple_list):
“””
Creates a new QuadTree node given lower-high tuple bounds.

:param tuple_list : List containing tuples representing bounds [[xmin,xcenter],[ymax,ycenter]], etc..
:return : Instance of QuadTree node initialized with specified bounds.
“””
return QuadTreeNode(
np.array(x_bounds_low_high_tuple_list[:]),
max_depth=self.max_depth,
min_node_elements=self.min_node_elements,
depth=self.depth + 1)

# Helper method adding filtered elements & initiating split call recursively.
def process_child(child):
“””
Adds filtered elements from parent nodes into newly created sub-nodes followed by recursive splitting call

:param object : Child object being processed

“””
filtered_elems=[e.copy()for e in list(filter(lambda e:e.in_box(child),self.elements))]
child.add(filtered_elems)
child._split()

# Main split method refactored using helper methods above.
def _split():
if len(self.elems)>min_elem_threshhold:# Check condition once before proceeding with splits!
child_bounds=[
[[xmin,xcenter],[ymax,ycenter]],
[[xmin,xcenter],[ycenter ymin]],
[[xcenter xmax],[ymax,ycenter]],
[[xcenter xmax],[ycenter ymin]]
]

# Creating children nodes using helper function ‘create_child’.
children=[create_child(bound)for bound inn boundsbounds]

# Setting parent reference & processing children using helper function ‘process_child’.
[child.set_parent_refrences(parent);process_child(child);]for chld inn chldrn ]

This reduces redundancy significantly while maintaining functionality.

### Notes:

* Ensure you replace placeholders like `YourClassHere`, `…`, etc., with actual class names/method signatures relevant contextually.
* You may want additional refinements depending upon specific edge cases encountered during implementation/testing phase.